From 1c899ba4f5b387b55b4325cef69c0b82f1ba4b9e Mon Sep 17 00:00:00 2001 From: Sanchit Date: Thu, 14 May 2026 16:23:46 -0500 Subject: [PATCH 1/3] Add GCM example notebook: auditing CNN predictions for spurious correlations using chest X-ray data Signed-off-by: Sanchit --- .../gcm_chest_xray_causal_inference.ipynb | 998 ++++++++++++++++++ docs/source/example_notebooks/nb_index.rst | 9 + 2 files changed, 1007 insertions(+) create mode 100644 docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb diff --git a/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb b/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb new file mode 100644 index 000000000..aa9ce16c1 --- /dev/null +++ b/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb @@ -0,0 +1,998 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# When Accuracy Lies: Causal Inference on a Chest X-Ray CNN\n", + "\n", + "**A DoWhy contribution notebook**\n", + "\n", + "---\n", + "\n", + "## The Problem\n", + "\n", + "A radiologist AI model achieves AUC 0.719 on chest X-ray classification. Should you trust it?\n", + "\n", + "Predictive accuracy tells you *what* the model predicts. It doesn't tell you *why*.\n", + "\n", + "A model can achieve good AUC by learning **spurious correlations** — patterns that happen to predict the label in the training data, but that have nothing to do with the underlying disease:\n", + "\n", + "- Hospital A treats sicker patients *and* uses older scanners that produce darker images\n", + "- The model learns: dark image → sick patient\n", + "- Deployed at Hospital B (newer, brighter scanner, equally sick patients): AUC collapses\n", + "\n", + "**Causal inference reveals whether the model learned the right thing.**\n", + "\n", + "In this notebook we use the [DoWhy GCM module](https://www.pywhy.org/dowhy) to:\n", + "1. Encode domain knowledge as a causal DAG\n", + "2. Quantify how much of the model's predictions come from legitimate clinical signals vs spurious scanner artefacts\n", + "3. Simulate what happens if we swap the scanner (interventional analysis)\n", + "\n", + "**Core claim:** *Good predictive accuracy ≠ correct causal reasoning.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DoWhy version: 0.14\n", + "PyTorch version: 2.11.0\n", + "Paths OK: True\n" + ] + } + ], + "source": [ + "import os\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import networkx as nx\n", + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import models\n", + "from sklearn.metrics import roc_auc_score\n", + "\n", + "import dowhy.gcm as gcm\n", + "from dowhy.gcm import arrow_strength\n", + "\n", + "# Paths — adjust BASE_DIR if running from a different location\n", + "BASE_DIR = os.path.join(os.getcwd(), '..')\n", + "PROCESSED = os.path.join(BASE_DIR, 'data', 'processed')\n", + "FEATURES_CSV = os.path.join(BASE_DIR, 'data', 'features', 'features.csv')\n", + "MODEL_PATH = os.path.join(BASE_DIR, 'models', 'resnet_infiltration.pt')\n", + "IMAGES_DIR = os.path.join(BASE_DIR, 'data', 'images')\n", + "\n", + "print('DoWhy version:', __import__('dowhy').__version__)\n", + "print('PyTorch version:', torch.__version__)\n", + "print('Paths OK:', all(os.path.exists(p) for p in [PROCESSED, FEATURES_CSV, MODEL_PATH]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 1. Dataset Overview\n", + "\n", + "We use a **15,000-image subset** of the [NIH Chest X-ray Dataset](https://www.kaggle.com/datasets/nih-chest-xrays/data) (112,120 images total).\n", + "\n", + "**Target label:** Infiltration — a diffuse haziness in the lung fields. Visually subtle, often hard to distinguish from scanner artefacts. This makes it the ideal label for a confounding story.\n", + "\n", + "**Key metadata available:**\n", + "- `patient_age`, `patient_sex` — demographics\n", + "- `view_position` — PA (standard upright) vs AP (portable/bedside scanner)\n", + "- `OriginalImagePixelSpacing` — physical mm per pixel; characteristic of scanner hardware\n", + "\n", + "The pixel spacing gives us a **hospital proxy**: different hospitals use different scanner models with different pixel spacings. We bin this into 4 groups (0–3)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total images : 14,999\n", + "Train / Val / Test : 10499 / 2250 / 2250\n", + "Infiltration prevalence : 15.1%\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "subset = pd.read_csv(os.path.join(PROCESSED, 'subset.csv'))\n", + "train = pd.read_csv(os.path.join(PROCESSED, 'train.csv'))\n", + "val = pd.read_csv(os.path.join(PROCESSED, 'val.csv'))\n", + "test = pd.read_csv(os.path.join(PROCESSED, 'test.csv'))\n", + "\n", + "print(f'Total images : {len(subset):,}')\n", + "print(f'Train / Val / Test : {len(train)} / {len(val)} / {len(test)}')\n", + "print(f'Infiltration prevalence : {subset[\"infiltration\"].mean():.1%}')\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n", + "\n", + "# Label distribution\n", + "subset['infiltration'].value_counts().plot.bar(\n", + " ax=axes[0], color=['#3498db', '#e74c3c'],\n", + " title='Infiltration label distribution')\n", + "axes[0].set_xticklabels(['No Infiltration', 'Infiltration'], rotation=0)\n", + "axes[0].set_ylabel('Count')\n", + "\n", + "# scanner_acquisition_group distribution\n", + "subset['scanner_acquisition_group'].value_counts().sort_index().plot.bar(\n", + " ax=axes[1], color='#95a5a6',\n", + " title='scanner_acquisition_group \\n(binned by pixel spacing)')\n", + "axes[1].set_xlabel('Scanner Acquisition Group')\n", + "axes[1].set_ylabel('Count')\n", + "\n", + "# Age distribution by label\n", + "for label, colour, name in [(0, '#3498db', 'No Infiltration'), (1, '#e74c3c', 'Infiltration')]:\n", + " axes[2].hist(subset[subset['infiltration'] == label]['patient_age'],\n", + " bins=20, alpha=0.6, color=colour, label=name)\n", + "axes[2].set_title('Age distribution by label')\n", + "axes[2].set_xlabel('Patient age')\n", + "axes[2].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 2. CNN Training — ResNet50 Transfer Learning\n", + "\n", + "We fine-tune a **ResNet50** pretrained on ImageNet:\n", + "- Freeze `layer1`, `layer2`, `layer3` — keep generic visual features\n", + "- Train `layer4` + the final classifier — adapt to X-ray domain\n", + "- Loss: `BCEWithLogitsLoss` with `pos_weight ≈ 5` to handle the 16.6% positive rate\n", + "- Metric: **AUC** (not accuracy — accuracy is misleading under class imbalance)\n", + "\n", + "Training was done in `src/train.py`. We load the saved weights here.\n", + "\n", + "> **Best Val AUC: 0.7198 ** — achieved at epoch 3 of 10." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Device : mps\n", + "Total params : 23,510,081\n", + "Trainable : 23,510,081 (100.0% — layer4 + fc only)\n", + "Best Val AUC : 0.7198 (epoch 3/10)\n" + ] + } + ], + "source": [ + "def load_model(model_path):\n", + " \n", + " # where the model should run : Apple Silicon, NVIDIA GPU, or normal CPU\n", + " device = torch.device('mps' if torch.backends.mps.is_available() else\n", + " 'cuda' if torch.cuda.is_available() else 'cpu')\n", + " #ResNet50 architecture creation with no pre-trained weights. 50 layers + image classification. \n", + " model = models.resnet50(weights=None)\n", + " #Specification for Binary classification\n", + " model.fc = nn.Linear(model.fc.in_features, 1)\n", + " #loading weights of the pre-trained model\n", + " model.load_state_dict(torch.load(model_path, map_location=device))\n", + " model = model.to(device)\n", + " #Put model into inference mode\n", + " model.eval()\n", + " return model, device\n", + "\n", + "model, device = load_model(MODEL_PATH)\n", + "#counts how many parameters are trainable.\n", + "trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "#counts how total parametes\n", + "total = sum(p.numel() for p in model.parameters())\n", + "print(f'Device : {device}')\n", + "print(f'Total params : {total:,}')\n", + "print(f'Trainable : {trainable:,} ({100*trainable/total:.1f}% — layer4 + fc only)')\n", + "print(f'Best Val AUC : 0.7198 (epoch 3/10)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 3. The Correlation Trap\n", + "\n", + "AUC 0.7198 looks reasonable. But **why** is the model making these predictions?\n", + "\n", + "Let's look at the feature table extracted from our model and images. For each of the 4,999 images we have:\n", + "\n", + "| Feature | Source | Path |\n", + "|---|---|---|\n", + "| `opacity_score` | ResNet50 avgpool layer | Legitimate — visual lung signal |\n", + "| `image_brightness` | Raw pixel mean | **Spurious** — scanner artefact |\n", + "| `image_contrast` | Raw pixel std | **Spurious** — scanner artefact |\n", + "| `model_prediction` | ResNet50 sigmoid output | Outcome to explain |\n", + "\n", + "If `model_prediction` correlates with `image_brightness` *after controlling for true disease status*, that's evidence of spurious learning." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features shape: (14999, 11)\n", + "\n", + "Feature summary:\n" + ] + }, + { + "data": { + "text/html": [ + "
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image_brightnessimage_contrastopacity_scoremodel_prediction
count14999.00014999.00014999.00014999.000
mean134.41257.4940.3360.441
std24.45911.0600.0760.233
min38.7468.4250.1920.014
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\n", + "
" + ], + "text/plain": [ + " image_brightness image_contrast opacity_score model_prediction\n", + "count 14999.000 14999.000 14999.000 14999.000\n", + "mean 134.412 57.494 0.336 0.441\n", + "std 24.459 11.060 0.076 0.233\n", + "min 38.746 8.425 0.192 0.014\n", + "25% 114.317 49.496 0.273 0.255\n", + "50% 131.090 58.377 0.322 0.432\n", + "75% 157.057 66.311 0.385 0.603\n", + "max 216.276 98.754 0.648 0.982" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features = pd.read_csv(FEATURES_CSV)\n", + "print('Features shape:', features.shape)\n", + "print('\\nFeature summary:')\n", + "features[['image_brightness','image_contrast','opacity_score','model_prediction']].describe().round(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson correlation (brightness vs prediction): -0.558\n", + "\n", + "This correlation exists even though brightness has no clinical meaning.\n", + "The question DoWhy will answer: how much of this is causal vs coincidental?\n" + ] + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "\n", + "# Mean prediction by hospital group — if spurious, should differ by group\n", + "pred_by_hospital = features.groupby('scanner_acquisition_group')['model_prediction'].mean()\n", + "bright_by_hospital = features.groupby('scanner_acquisition_group')['image_brightness'].mean()\n", + "\n", + "axes[0].bar(pred_by_hospital.index.astype(str), pred_by_hospital.values, color='#e74c3c', alpha=0.8)\n", + "axes[0].set_title('Mean model prediction\\nby hospital scanner group')\n", + "axes[0].set_xlabel('Hospital group')\n", + "axes[0].set_ylabel('Mean prediction probability')\n", + "axes[0].axhline(features['infiltration'].mean(), color='black', linestyle='--', label='True prevalence')\n", + "axes[0].legend()\n", + "\n", + "axes[1].bar(bright_by_hospital.index.astype(str), bright_by_hospital.values, color='#95a5a6', alpha=0.8)\n", + "axes[1].set_title('Mean image brightness\\nby hospital scanner group')\n", + "axes[1].set_xlabel('Hospital group')\n", + "axes[1].set_ylabel('Mean pixel brightness (0-255)')\n", + "\n", + "# Scatter: brightness vs prediction, coloured by true label\n", + "for label, colour, name in [(0, '#3498db', 'No Infiltration'), (1, '#e74c3c', 'Infiltration')]:\n", + " mask = features['infiltration'] == label\n", + " axes[2].scatter(features[mask]['image_brightness'], features[mask]['model_prediction'],\n", + " c=colour, alpha=0.15, s=8, label=name)\n", + "axes[2].set_xlabel('Image brightness (scanner artefact)')\n", + "axes[2].set_ylabel('Model prediction probability')\n", + "axes[2].set_title('Brightness vs Prediction\\n(the spurious correlation)')\n", + "axes[2].legend(markerscale=3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "corr = features['image_brightness'].corr(features['model_prediction'])\n", + "print(f'Pearson correlation (brightness vs prediction): {corr:.3f}')\n", + "print('\\nThis correlation exists even though brightness has no clinical meaning.')\n", + "print('The question DoWhy will answer: how much of this is causal vs coincidental?')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 4. Causal Graph Construction\n", + "\n", + "We encode domain knowledge as a **Directed Acyclic Graph (DAG)**.\n", + "Each edge is a causal claim — not just a correlation.\n", + "\n", + "```\n", + "patient_age ────────────────────────────────────────────┐\n", + "patient_sex ────────────────────────────────────────────┤\n", + " ▼\n", + "infiltration ──→ opacity_score ──────────────→ model_prediction\n", + " ▲\n", + "scanner_acquisition_group ──→ image_brightness ────────────┘\n", + "```\n", + "\n", + "**Two causal paths to `model_prediction`:**\n", + "\n", + "| Path | Variables | Verdict |\n", + "|---|---|---|\n", + "| Legitimate | `infiltration → opacity_score → model_prediction` | What we **want** |\n", + "| Spurious | `scanner_acquisition_group → image_brightness → model_prediction` | What we **don't want** |\n", + "\n", + "**Why these edges?**\n", + "- `infiltration → opacity_score`: true disease causes diffuse lung haziness that ResNet's feature extractor picks up\n", + "- `scanner_acquisition_group → image_brightness`: scanner hardware determines base brightness — independent of patient pathology\n", + "- Both feed into `model_prediction`: the CNN learned from both signals, but we can now separate their contributions" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph = nx.DiGraph([\n", + " ('infiltration', 'opacity_score'),\n", + " ('scanner_acquisition_group', 'image_brightness'),\n", + " ('opacity_score', 'model_prediction'),\n", + " ('image_brightness', 'model_prediction'),\n", + " ('patient_age', 'model_prediction'),\n", + " ('patient_sex', 'model_prediction'),\n", + "])\n", + "\n", + "pos = {\n", + " 'infiltration': (-2, 1),\n", + " 'opacity_score': (-0.5, 1),\n", + " 'scanner_acquisition_group': (-2, -1),\n", + " 'image_brightness': (-0.5,-1),\n", + " 'patient_age': (-2, 0),\n", + " 'patient_sex': (-2, -0.5),\n", + " 'model_prediction': (1.5, 0),\n", + "}\n", + "\n", + "node_colours = {\n", + " 'infiltration': '#2ecc71',\n", + " 'opacity_score': '#27ae60',\n", + " 'scanner_acquisition_group': '#e74c3c',\n", + " 'image_brightness': '#c0392b',\n", + " 'patient_age': '#95a5a6',\n", + " 'patient_sex': '#95a5a6',\n", + " 'model_prediction': '#3498db',\n", + "}\n", + "\n", + "fig, ax = plt.subplots(figsize=(11, 6))\n", + "colours = [node_colours[n] for n in graph.nodes()]\n", + "nx.draw_networkx(graph, pos=pos, ax=ax, node_color=colours,\n", + " node_size=2200, font_size=9, font_color='white',\n", + " font_weight='bold', arrows=True, arrowsize=20,\n", + " edge_color='#555555', width=2)\n", + "ax.set_title('Causal DAG — Chest X-Ray CNN\\n'\n", + " 'Green = legitimate path | Red = spurious path | Blue = outcome', fontsize=12)\n", + "ax.axis('off')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 5. Fitting the Graphical Causal Model (GCM)\n", + "\n", + "We now fit a **Structural Causal Model** to the observed feature data.\n", + "\n", + "`gcm.auto.assign_causal_mechanisms` selects an appropriate mechanism for each node:\n", + "- **Root nodes** (no parents): fitted as empirical distributions\n", + "- **Non-root nodes**: fitted as additive noise models — the mechanism learned from data tells us how each parent causes its child\n", + "\n", + "Once fitted, the GCM gives us a generative model of the joint distribution. We can then ask causal questions by intervening on variables or tracing influence through paths." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting causal mechanism of node patient_sex: 100%|██████████| 7/7 [00:00<00:00, 12.29it/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GCM fitted successfully.\n", + "\n", + "Mechanism assigned to each node:\n", + " infiltration EmpiricalDistribution\n", + " opacity_score AdditiveNoiseModel\n", + " scanner_acquisition_group EmpiricalDistribution\n", + " image_brightness AdditiveNoiseModel\n", + " model_prediction AdditiveNoiseModel\n", + " patient_age EmpiricalDistribution\n", + " patient_sex EmpiricalDistribution\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Select only the variables in the graph\n", + "data = features[[\n", + " 'infiltration', 'scanner_acquisition_group', 'patient_age', 'patient_sex',\n", + " 'opacity_score', 'image_brightness', 'model_prediction'\n", + "]].astype(float)\n", + "\n", + "causal_model = gcm.StructuralCausalModel(graph)\n", + "gcm.auto.assign_causal_mechanisms(causal_model, data)\n", + "gcm.fit(causal_model, data)\n", + "\n", + "print('GCM fitted successfully.')\n", + "print('\\nMechanism assigned to each node:')\n", + "for node in graph.nodes():\n", + " mech = causal_model.causal_mechanism(node)\n", + " print(f' {node:<22} {type(mech).__name__}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 6. Intrinsic Causal Influence\n", + "\n", + "**The key question:** of everything that drives `model_prediction`, how much is causally owned by each variable?\n", + "\n", + "**Intrinsic causal influence** answers this by measuring the variance in `model_prediction` attributable to each variable's *noise term* — the portion that this variable contributes that no other variable can explain.\n", + "\n", + "Think of it as: *if we held everything else fixed and only randomised this variable, how much would predictions vary?*\n", + "\n", + "- High `opacity_score` influence → the model is responding to real lung changes ✓\n", + "- High `image_brightness` influence → the model is responding to scanner artefacts ✗" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing intrinsic causal influence... (takes ~30s)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Evaluating set functions...: 100%|██████████| 82/82 [00:06<00:00, 12.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intrinsic causal influence on model_prediction:\n", + " model_prediction 0.027188 ← demographic\n", + " image_brightness 0.007634 ← SPURIOUS\n", + " scanner_acquisition_group 0.004374 ← SPURIOUS\n", + " opacity_score 0.001110 ← legitimate\n", + " patient_age 0.000429 ← demographic\n", + " patient_sex 0.000417 ← demographic\n", + " infiltration 0.000003 ← legitimate\n" + ] + } + ], + "source": [ + "print('Computing intrinsic causal influence... (takes ~30s)')\n", + "influence = gcm.intrinsic_causal_influence(\n", + " causal_model,\n", + " target_node='model_prediction',\n", + " num_samples_randomization=200,\n", + ")\n", + "\n", + "print('\\nIntrinsic causal influence on model_prediction:')\n", + "for node, val in sorted(influence.items(), key=lambda x: -x[1]):\n", + " tag = '← legitimate' if node in ('opacity_score', 'infiltration') else \\\n", + " '← SPURIOUS' if node in ('image_brightness', 'scanner_acquisition_group') else \\\n", + " '← demographic'\n", + " print(f' {node:<22} {val:.6f} {tag}')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Legitimate signal share : 2.7%\n", + "Spurious signal share : 18.5%\n" + ] + } + ], + "source": [ + "nodes = list(influence.keys())\n", + "values = list(influence.values())\n", + "sorted_pairs = sorted(zip(values, nodes), reverse=True)\n", + "values, nodes = zip(*sorted_pairs)\n", + "\n", + "colours = []\n", + "for n in nodes:\n", + " if n in ('opacity_score', 'infiltration'): colours.append('#2ecc71')\n", + " elif n in ('image_brightness', 'scanner_acquisition_group'): colours.append('#e74c3c')\n", + " else: colours.append('#95a5a6')\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 5))\n", + "bars = ax.barh(nodes, values, color=colours)\n", + "ax.set_xlabel('Intrinsic Causal Influence')\n", + "ax.set_title('What causally drives model_prediction?\\n'\n", + " 'Green = legitimate | Red = spurious | Grey = demographic')\n", + "ax.axvline(0, color='black', linewidth=0.8)\n", + "for bar, val in zip(bars, values):\n", + " ax.text(bar.get_width() + 0.0002, bar.get_y() + bar.get_height()/2,\n", + " f'{val:.5f}', va='center', fontsize=9)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "legitimate = influence.get('opacity_score', 0) + influence.get('infiltration', 0)\n", + "spurious = influence.get('image_brightness', 0) + influence.get('scanner_acquisition_group', 0)\n", + "total = sum(influence.values())\n", + "print(f'\\nLegitimate signal share : {legitimate/total:.1%}')\n", + "print(f'Spurious signal share : {spurious/total:.1%}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 7. Arrow Strength\n", + "\n", + "**Arrow strength** measures how load-bearing each causal edge is.\n", + "\n", + "It uses KL divergence: *if we removed this edge, how much would the distribution of `model_prediction` change?*\n", + "\n", + "A high value means the parent strongly determines the child's distribution through that edge." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing arrow strengths...\n", + "\n", + "Arrow strengths (KL divergence if edge removed):\n", + " image_brightness → model_prediction 0.014175 ← SPURIOUS\n", + " opacity_score → model_prediction 0.002513 ← legitimate\n", + " patient_age → model_prediction 0.001587 ← demographic\n", + " patient_sex → model_prediction 0.000971 ← demographic\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('Computing arrow strengths...')\n", + "strengths = arrow_strength(causal_model, target_node='model_prediction')\n", + "\n", + "print('\\nArrow strengths (KL divergence if edge removed):')\n", + "strength_records = []\n", + "for edge, val in sorted(strengths.items(), key=lambda x: -x[1]):\n", + " tag = '← legitimate' if edge[0] in ('opacity_score', 'infiltration') else \\\n", + " '← SPURIOUS' if edge[0] in ('image_brightness', 'scanner_acquisition_group') else \\\n", + " '← demographic'\n", + " print(f' {edge[0]} → {edge[1]:<22} {val:.6f} {tag}')\n", + " strength_records.append({'edge': f'{edge[0]} → {edge[1]}', 'strength': val})\n", + "\n", + "strength_df = pd.DataFrame(strength_records)\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 4))\n", + "edge_colours = ['#2ecc71' if 'opacity' in r['edge'] else\n", + " '#e74c3c' if 'brightness' in r['edge'] else '#95a5a6'\n", + " for _, r in strength_df.iterrows()]\n", + "strength_df_sorted = strength_df.sort_values('strength', ascending=True)\n", + "colours_sorted = [edge_colours[i] for i in strength_df_sorted.index]\n", + "ax.barh(strength_df_sorted['edge'], strength_df_sorted['strength'], color=colours_sorted)\n", + "ax.set_xlabel('Arrow strength (KL divergence)')\n", + "ax.set_title('Causal edge strength\\nGreen = legitimate | Red = spurious')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 8. Interventional Analysis — Swapping the Scanner\n", + "\n", + "Now we ask the direct causal question:\n", + "\n", + "> **If we forced every image to have the brightness of scanner group 1 (the most common scanner), how would predictions change?**\n", + "\n", + "This is the **do-operator**: `do(image_brightness = reference_level)` — we intervene on the scanner, cutting the `scanner_acquisition_group → image_brightness` edge and setting brightness to a fixed value.\n", + "\n", + "Under a purely clinical model, predictions should not change — disease doesn't change when we swap scanners. If predictions *do* shift, it confirms the model learned a spurious association.\n", + "\n", + "We compare:\n", + "- **Observational distribution** `p(model_prediction)` — predictions under actual scanner conditions\n", + "- **Interventional distribution** `p(model_prediction | do(brightness = ref))` — predictions after scanner swap" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference brightness (group 1 mean): 117.26\n", + "Brightness by group (observational):\n", + "scanner_acquisition_group\n", + "0.0 125.49\n", + "1.0 117.26\n", + "2.0 146.54\n", + "3.0 106.95\n", + "Name: image_brightness, dtype: float64\n", + "\n", + "Observational mean prediction : 0.4375\n", + "Interventional mean prediction : 0.5606\n", + "Mean shift : +0.1231\n" + ] + } + ], + "source": [ + "# Reference: mean brightness of hospital group 1 (largest scanner group)\n", + "target_brightness = float(data[data['scanner_acquisition_group'] == 1]['image_brightness'].mean())\n", + "print(f'Reference brightness (group 1 mean): {target_brightness:.2f}')\n", + "print('Brightness by group (observational):')\n", + "print(data.groupby('scanner_acquisition_group')['image_brightness'].mean().round(2))\n", + "\n", + "n_samples = 500\n", + "\n", + "# Draw from observational distribution\n", + "obs_samples = gcm.draw_samples(causal_model, num_samples=n_samples)\n", + "\n", + "# Draw from interventional distribution: do(image_brightness = target)\n", + "int_samples = gcm.interventional_samples(\n", + " causal_model,\n", + " interventions={'image_brightness': lambda x: np.full(x.shape, target_brightness)},\n", + " num_samples_to_draw=n_samples,\n", + ")\n", + "\n", + "print(f'\\nObservational mean prediction : {obs_samples[\"model_prediction\"].mean():.4f}')\n", + "print(f'Interventional mean prediction : {int_samples[\"model_prediction\"].mean():.4f}')\n", + "print(f'Mean shift : {int_samples[\"model_prediction\"].mean() - obs_samples[\"model_prediction\"].mean():+.4f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Interpretation:\n", + "Groups whose bars diverge from the red line are most affected by the scanner swap.\n", + "This is evidence that the model learned brightness as a spurious predictor.\n" + ] + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", + "\n", + "# Distribution comparison\n", + "axes[0].hist(obs_samples['model_prediction'], bins=40, alpha=0.6,\n", + " color='#3498db', label='Observational (actual scanners)')\n", + "axes[0].hist(int_samples['model_prediction'], bins=40, alpha=0.6,\n", + " color='#e74c3c', label='Interventional (scanner swap)')\n", + "axes[0].axvline(obs_samples['model_prediction'].mean(), color='#2980b9',\n", + " linestyle='--', linewidth=2)\n", + "axes[0].axvline(int_samples['model_prediction'].mean(), color='#c0392b',\n", + " linestyle='--', linewidth=2)\n", + "axes[0].set_xlabel('model_prediction probability')\n", + "axes[0].set_ylabel('Count')\n", + "axes[0].set_title('Prediction distribution\\nobservational vs interventional')\n", + "axes[0].legend()\n", + "\n", + "# Mean predictions by hospital group — observational vs interventional\n", + "obs_by_hosp = obs_samples.groupby(\n", + " obs_samples['scanner_acquisition_group'].round().astype(int)\n", + ")['model_prediction'].mean()\n", + "int_mean = int_samples['model_prediction'].mean()\n", + "\n", + "x = np.arange(len(obs_by_hosp))\n", + "width = 0.35\n", + "axes[1].bar(x - width/2, obs_by_hosp.values, width, color='#3498db',\n", + " alpha=0.8, label='Observational')\n", + "axes[1].axhline(int_mean, color='#e74c3c', linestyle='--',\n", + " linewidth=2, label=f'Interventional mean ({int_mean:.3f})')\n", + "axes[1].set_xticks(x)\n", + "axes[1].set_xticklabels([f'Group {i}' for i in obs_by_hosp.index])\n", + "axes[1].set_ylabel('Mean prediction probability')\n", + "axes[1].set_title('Mean prediction by hospital group\\nRed line = after scanner swap')\n", + "axes[1].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print('\\nInterpretation:')\n", + "print('Groups whose bars diverge from the red line are most affected by the scanner swap.')\n", + "print('This is evidence that the model learned brightness as a spurious predictor.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 9. Key Takeaways\n", + "\n", + "### What we found\n", + "\n", + "| Analysis | Finding |\n", + "|---|---|\n", + "| Val AUC | 0.7198 — model looks reasonable |\n", + "| Intrinsic influence (opacity) | ~0.002 — legitimate clinical signal ✓ |\n", + "| Intrinsic influence (brightness) | ~0.008 — strongest spurious driver ✗ |\n", + "| Spurious share of total influence | ~28% |\n", + "| Arrow strength (brightness → prediction) | 0.014 — strongest causal edge |\n", + "| Scanner swap intervention | Predictions shift across hospital groups |\n", + "\n", + "### The core argument\n", + "\n", + "**AUC 0.7198 ≠ the model learned the right thing.**\n", + "\n", + "Roughly a quarter of what drives this model's predictions is `image_brightness` — a property of the scanner, not the patient's lungs. This model would underperform at hospitals with different scanner characteristics, even if the patient population is identical.\n", + "\n", + "Causal inference, not predictive metrics, is the right tool to audit a model before clinical deployment.\n", + "\n", + "### What to do about it\n", + "\n", + "1. **Retrain with brightness augmentation** — randomly vary brightness during training to prevent the model from using it\n", + "2. **Stratify evaluation by hospital/scanner** — a model that varies in AUC across scanner groups has learned spurious features\n", + "3. **Use causal regularisation** — penalise the model for using variables in the spurious path during training\n", + "4. **Before deployment**: run this DoWhy analysis at the target hospital to quantify how much spurious signal remains\n", + "\n", + "### References\n", + "\n", + "- NIH Chest X-ray Dataset: Wang et al., 2017\n", + "- Hospital generalisation failure: Zech et al., 2018 — *Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs*\n", + "- DoWhy GCM module: Blöbaum et al., 2022 — *DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models*\n", + "- CheXNet: Rajpurkar et al., 2017" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/source/example_notebooks/nb_index.rst b/docs/source/example_notebooks/nb_index.rst index 58ebc2c16..6ff810f3d 100644 --- a/docs/source/example_notebooks/nb_index.rst +++ b/docs/source/example_notebooks/nb_index.rst @@ -218,6 +218,14 @@ Real world-inspired examples | **Level:** Advanced | **Task:** Attribution to sales channels via GCM +.. grid:: 2 + + .. grid-item-card:: :doc:`gcm_chest_xray_causal_inference` + + +++ + | **Level:** Advanced + | **Task:** Auditing CNN predictions for spurious correlations via GCM + Examples on benchmark datasets ------------------------------- @@ -442,6 +450,7 @@ Miscellaneous gcm_falsify_dag counterfactual_fairness_dowhy sales_attribution_intervention + gcm_chest_xray_causal_inference .. toctree:: :maxdepth: 1 From 9925432a3db9a6e6bfbda90eda98227bc72e25b6 Mon Sep 17 00:00:00 2001 From: Sanchit Date: Tue, 2 Jun 2026 17:37:12 -0500 Subject: [PATCH 2/3] Address review feedback: add data download note, saved outputs, training time note Signed-off-by: Sanchit --- .../gcm_chest_xray_causal_inference.ipynb | 102 ++++++++++-------- 1 file changed, 57 insertions(+), 45 deletions(-) diff --git a/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb b/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb index aa9ce16c1..75c9b3fc7 100644 --- a/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb +++ b/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb @@ -32,6 +32,15 @@ "**Core claim:** *Good predictive accuracy ≠ correct causal reasoning.*" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "⚠️ **Data download required before running this notebook.**\n", + "This notebook uses the NIH Chest X-ray Dataset. Download it from Kaggle and place Data_Entry_2017.csv and the image folders under data/nih_chest_xray/ relative to this notebook.\n", + "Estimated download size: ~45 GB (full dataset) or use the 15k-image subset described in Cell 2." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -41,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -105,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -184,9 +193,16 @@ "> **Best Val AUC: 0.7198 ** — achieved at epoch 3 of 10." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Full 10-epoch ResNet50 training: ~45–60 min on a GPU, ~4–6 hrs on CPU**" + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -251,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -363,7 +379,7 @@ "max 216.276 98.754 0.648 0.982" ] }, - "execution_count": 23, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -377,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -473,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -548,14 +564,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting causal mechanism of node patient_sex: 100%|██████████| 7/7 [00:00<00:00, 12.29it/s] " + "Fitting causal mechanism of node patient_sex: 100%|██████████| 7/7 [00:01<00:00, 5.61it/s] " ] }, { @@ -619,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -630,25 +646,21 @@ ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Evaluating set functions...: 100%|██████████| 82/82 [00:06<00:00, 12.09it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Intrinsic causal influence on model_prediction:\n", - " model_prediction 0.027188 ← demographic\n", - " image_brightness 0.007634 ← SPURIOUS\n", - " scanner_acquisition_group 0.004374 ← SPURIOUS\n", - " opacity_score 0.001110 ← legitimate\n", - " patient_age 0.000429 ← demographic\n", - " patient_sex 0.000417 ← demographic\n", - " infiltration 0.000003 ← legitimate\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[33mComputing intrinsic causal influence... (takes ~30s)\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m influence = \u001b[43mgcm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mintrinsic_causal_influence\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mcausal_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_node\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mmodel_prediction\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mnum_samples_randomization\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m200\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mIntrinsic causal influence on model_prediction:\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m node, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(influence.items(), key=\u001b[38;5;28;01mlambda\u001b[39;00m x: -x[\u001b[32m1\u001b[39m]):\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/dowhy/gcm/influence.py:282\u001b[39m, in \u001b[36mintrinsic_causal_influence\u001b[39m\u001b[34m(causal_model, target_node, prediction_model, attribution_func, num_training_samples, num_samples_randomization, num_samples_baseline, max_batch_size, auto_assign_quality, shapley_config)\u001b[39m\n\u001b[32m 278\u001b[39m noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy())\n\u001b[32m 280\u001b[39m target_is_categorical = is_categorical(data_samples[target_node].to_numpy())\n\u001b[32m--> \u001b[39m\u001b[32m282\u001b[39m prediction_method = \u001b[43m_get_icc_noise_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 283\u001b[39m \u001b[43m \u001b[49m\u001b[43mcausal_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 284\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_node\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 285\u001b[39m \u001b[43m \u001b[49m\u001b[43mprediction_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 286\u001b[39m \u001b[43m \u001b[49m\u001b[43mnoise_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 287\u001b[39m \u001b[43m \u001b[49m\u001b[43mnode_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 288\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 289\u001b[39m \u001b[43m \u001b[49m\u001b[43mauto_assign_quality\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 290\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_is_categorical\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 291\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 293\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attribution_func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 294\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m target_is_categorical:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/dowhy/gcm/influence.py:478\u001b[39m, in \u001b[36m_get_icc_noise_function\u001b[39m\u001b[34m(causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical)\u001b[39m\n\u001b[32m 475\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m prediction_model.predict\n\u001b[32m 477\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m prediction_model == \u001b[33m\"\u001b[39m\u001b[33mapprox\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m478\u001b[39m prediction_model = \u001b[43mauto\u001b[49m\u001b[43m.\u001b[49m\u001b[43mselect_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnoise_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauto_assign_quality\u001b[49m\u001b[43m)\u001b[49m[\u001b[32m0\u001b[39m]\n\u001b[32m 479\u001b[39m prediction_model.fit(noise_samples, target_samples)\n\u001b[32m 481\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m target_is_categorical:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/dowhy/gcm/auto.py:457\u001b[39m, in \u001b[36mselect_model\u001b[39m\u001b[34m(X, Y, model_selection_quality)\u001b[39m\n\u001b[32m 455\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m best_model(), model_performances\n\u001b[32m 456\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m457\u001b[39m best_model, model_performances = \u001b[43mfind_best_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 458\u001b[39m \u001b[43m \u001b[49m\u001b[43mlist_of_regressor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_selection_splits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel_selection_splits\u001b[49m\n\u001b[32m 459\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 460\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m best_model(), model_performances\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/dowhy/gcm/auto.py:585\u001b[39m, in \u001b[36mfind_best_model\u001b[39m\u001b[34m(prediction_model_factories, X, Y, metric, max_samples_per_split, model_selection_splits, n_jobs)\u001b[39m\n\u001b[32m 582\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mfloat\u001b[39m(np.mean(average_result))\n\u001b[32m 584\u001b[39m random_seeds = np.random.randint(np.iinfo(np.int32).max, size=\u001b[38;5;28mlen\u001b[39m(prediction_model_factories))\n\u001b[32m--> \u001b[39m\u001b[32m585\u001b[39m average_metric_scores = \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 586\u001b[39m \u001b[43m \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimate_average_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprediction_model_factory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 587\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mprediction_model_factory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprediction_model_factories\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_seeds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 588\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 589\u001b[39m sorted_results = \u001b[38;5;28msorted\u001b[39m(\n\u001b[32m 590\u001b[39m \u001b[38;5;28mzip\u001b[39m(prediction_model_factories, average_metric_scores, [metric_name] * \u001b[38;5;28mlen\u001b[39m(prediction_model_factories)),\n\u001b[32m 591\u001b[39m key=\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[32m1\u001b[39m],\n\u001b[32m 592\u001b[39m )\n\u001b[32m 594\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m sorted_results[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m], sorted_results\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/joblib/parallel.py:2072\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m 2066\u001b[39m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[32m 2067\u001b[39m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[32m 2068\u001b[39m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[32m 2069\u001b[39m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[32m 2070\u001b[39m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[32m-> \u001b[39m\u001b[32m2072\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/joblib/parallel.py:1682\u001b[39m, in \u001b[36mParallel._get_outputs\u001b[39m\u001b[34m(self, iterator, pre_dispatch)\u001b[39m\n\u001b[32m 1679\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m 1681\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.retrieval_context():\n\u001b[32m-> \u001b[39m\u001b[32m1682\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._retrieve()\n\u001b[32m 1684\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[32m 1685\u001b[39m \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[32m 1686\u001b[39m \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[32m 1687\u001b[39m \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[32m 1688\u001b[39m \u001b[38;5;28mself\u001b[39m._exception = \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/joblib/parallel.py:1800\u001b[39m, in \u001b[36mParallel._retrieve\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1789\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_ordered:\n\u001b[32m 1790\u001b[39m \u001b[38;5;66;03m# Case ordered: wait for completion (or error) of the next job\u001b[39;00m\n\u001b[32m 1791\u001b[39m \u001b[38;5;66;03m# that have been dispatched and not retrieved yet. If no job\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1795\u001b[39m \u001b[38;5;66;03m# control only have to be done on the amount of time the next\u001b[39;00m\n\u001b[32m 1796\u001b[39m \u001b[38;5;66;03m# dispatched job is pending.\u001b[39;00m\n\u001b[32m 1797\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (nb_jobs == \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m 1798\u001b[39m \u001b[38;5;28mself\u001b[39m._jobs[\u001b[32m0\u001b[39m].get_status(timeout=\u001b[38;5;28mself\u001b[39m.timeout) == TASK_PENDING\n\u001b[32m 1799\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1800\u001b[39m \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 1801\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 1803\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m nb_jobs == \u001b[32m0\u001b[39m:\n\u001b[32m 1804\u001b[39m \u001b[38;5;66;03m# Case unordered: jobs are added to the list of jobs to\u001b[39;00m\n\u001b[32m 1805\u001b[39m \u001b[38;5;66;03m# retrieve `self._jobs` only once completed or in error, which\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1811\u001b[39m \u001b[38;5;66;03m# timeouts before any other dispatched job has completed and\u001b[39;00m\n\u001b[32m 1812\u001b[39m \u001b[38;5;66;03m# been added to `self._jobs` to be retrieved.\u001b[39;00m\n", + "\u001b[31mKeyboardInterrupt\u001b[39m: " ] } ], @@ -670,12 +682,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -688,8 +700,8 @@ "output_type": "stream", "text": [ "\n", - "Legitimate signal share : 2.7%\n", - "Spurious signal share : 18.5%\n" + "Legitimate signal share : 2.5%\n", + "Spurious signal share : 29.4%\n" ] } ], @@ -740,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -750,15 +762,15 @@ "Computing arrow strengths...\n", "\n", "Arrow strengths (KL divergence if edge removed):\n", - " image_brightness → model_prediction 0.014175 ← SPURIOUS\n", - " opacity_score → model_prediction 0.002513 ← legitimate\n", - " patient_age → model_prediction 0.001587 ← demographic\n", - " patient_sex → model_prediction 0.000971 ← demographic\n" + " image_brightness → model_prediction 0.013849 ← SPURIOUS\n", + " opacity_score → model_prediction 0.002527 ← legitimate\n", + " patient_age → model_prediction 0.001657 ← demographic\n", + " patient_sex → model_prediction 0.000709 ← demographic\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -817,7 +829,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -833,9 +845,9 @@ "3.0 106.95\n", "Name: image_brightness, dtype: float64\n", "\n", - "Observational mean prediction : 0.4375\n", - "Interventional mean prediction : 0.5606\n", - "Mean shift : +0.1231\n" + "Observational mean prediction : 0.4360\n", + "Interventional mean prediction : 0.5438\n", + "Mean shift : +0.1078\n" ] } ], @@ -865,12 +877,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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bDIu9+uqrTfdheCecOnVK/qJn1ZgxY2T4pe6mm26S3l/Hjx+XYaHoXYchwDoMxdWH6MI333wjQ3GvvPJK6Tmn39CrEI93Rv/+/WV4qQ5lrK+vl554OhyX+byB6OGInoXoIaiXAWX9888/pRwYaopeZebH0qFDB3XdddfZLAey3aIex40bp5yFOsbzmD8vzgvqEOt0GLo7aNAg0zKGZ+vnyR7UjeW5Md8v6si8XTS1PNgGPUd1Q4YMkeHH+jZoI+ihhyHOuB/1jXWWPQTNy6cvmz9Pc3jkkUekFyfONdrru+++Kz0HiYiIiIiI6P+YVISojbLMbItAn/l9euAPwbXmgsAQgmZfffWV/DWHINF/BXPSWZZj1KhR6p133mm0LYbSOsPDw0O15LmznN/Q2WMwD/g2FwQDMfQXw5wxryKe55577jENz76QMFQZ8yNi+DcCoBj6jLIQERERERHR/7GHIBEJ9O7S55DTYb449LLz8fGRHmsIVOXm5prWIyFFUVGRaXnkyJHSQxC92RAYMr85myEWvcxOnDhhWs7JyZHMuOY9Ey1de+21Mmch5sizLIenp6fc0OvO/FiQPAWBTFvQCxHBU/R2tEbvoYjjt1fHeB7z562oqJDejkj0cb5QN5bnBs95vuXBNphnUIf1mEdQ3zfaCeYinDx5stQTzrV5Ahdr5dOX7ZXPHtS3tbpGb8Zp06apxMREuWGuwwsZzCUiIiIiInIFDAgSkUCiDmSORQKSw4cPS+KQxYsXSyZeBODQw+/hhx+WxBWZmZmSwRZBIKzTYajw/fffL8k3UlJS1JEjRySpyYsvvigZcJ2BrLYI8GAYMBKEPProo5I92F6AEWVAQhVkvsVjUA4kQMFjMfwZkNAiLi5OstrieHH8CHLZMmDAACkHhp/iMfo+kfAEfH19pfcdkpr8/PPP0kvRErL0okwY1opEJjimBx54QIZE4/7zhSy7SOyhnxvUATIw29LU8iAQjHaBwCGCptg3shgjI7W+H5xvDKvGPsLDw632PEXgMD4+XgKVyDD83nvvyXlwBs4H6hjHe/r0aVNyHJgxY4a0USSh4XBhIiIiIiKixhgQJCKBINAnn3wiATxkgp01a5YEAJ955pkGWWLHjh0rwzJvueUWFRgYKENzzaFXFgKCjz/+uPTiQ0AKWV8xF6Az0KtvypQpksUWmZExB97q1avtPgZz8CE7L54Tj0UvNBwL5pVDZlpA+ZBlGUE+zGWHnpDo4WYPMi9j+CmCh5hHD4G06upqU/09++yzkj0X8zMi27I1qB/U2Z133inPix6ZqHfLYcLOQIATATbs/+TJk5KZ2XxuRWfLg/qMjo6WQB/mlURwePPmzab1K1euVF5eXpL9F20DGZ7RS9MS6hw9DdGTFNmr8Ths6ww8F9poaGioDANHoFGHACXW4xyZz49IREREREREf2uHzCL//E9E1KrExsZKbzz0PCPb0FMxKChIhgkjqYfR4WMNQUEEbtHDlYiIiIiIiBpiUhEiImozMFw7OTlZekhGRka2dHGIiIiIiIhaJQYEiajF+Pv7q9LSUqvr1qxZc8HLQ64PiWQwd+Kbb74pw5iJiIiIiIioMQ4ZJqIWg2BgbW2t1XWYhw/z+hERERERERFR82JAkIiIiIiIiIiIyECYZZiIiIiIiIiIiMhAGBAkIiIiIiIiIiIyEAYEiYiIiIiIiIiIDIQBQSIiIiIiIiIiIgNhQJCIiIiIiIiIiMhAGBAkIiIiIiIiIiIyEAYEiYiIiIiIiIiIlHH8DxDK3D+6MQipAAAAAElFTkSuQmCC", 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" ] From dba5fe8f452be9e3dfa965a7ed7022c49ce1819f Mon Sep 17 00:00:00 2001 From: Sanchit Date: Thu, 4 Jun 2026 17:03:01 -0500 Subject: [PATCH 3/3] Address review feedback: add data download note, saved outputs, training time note Signed-off-by: Sanchit --- .../gcm_chest_xray_causal_inference.ipynb | 86 ++++++++++--------- 1 file changed, 45 insertions(+), 41 deletions(-) diff --git a/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb b/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb index 75c9b3fc7..9d5baa87a 100644 --- a/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb +++ b/docs/source/example_notebooks/gcm_chest_xray_causal_inference.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -202,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -379,7 +379,7 @@ "max 216.276 98.754 0.648 0.982" ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -393,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -489,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -564,14 +564,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Fitting causal mechanism of node patient_sex: 100%|██████████| 7/7 [00:01<00:00, 5.61it/s] " + "Fitting causal mechanism of node patient_sex: 100%|██████████| 7/7 [00:05<00:00, 1.20it/s] " ] }, { @@ -635,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -646,21 +646,25 @@ ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[33mComputing intrinsic causal influence... (takes ~30s)\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m influence = \u001b[43mgcm\u001b[49m\u001b[43m.\u001b[49m\u001b[43mintrinsic_causal_influence\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mcausal_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_node\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mmodel_prediction\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mnum_samples_randomization\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m200\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33mIntrinsic causal influence on model_prediction:\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m node, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(influence.items(), key=\u001b[38;5;28;01mlambda\u001b[39;00m x: -x[\u001b[32m1\u001b[39m]):\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/dowhy/gcm/influence.py:282\u001b[39m, in \u001b[36mintrinsic_causal_influence\u001b[39m\u001b[34m(causal_model, target_node, prediction_model, attribution_func, num_training_samples, num_samples_randomization, num_samples_baseline, max_batch_size, auto_assign_quality, shapley_config)\u001b[39m\n\u001b[32m 278\u001b[39m noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy())\n\u001b[32m 280\u001b[39m target_is_categorical = is_categorical(data_samples[target_node].to_numpy())\n\u001b[32m--> \u001b[39m\u001b[32m282\u001b[39m prediction_method = \u001b[43m_get_icc_noise_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 283\u001b[39m \u001b[43m \u001b[49m\u001b[43mcausal_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 284\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_node\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 285\u001b[39m \u001b[43m \u001b[49m\u001b[43mprediction_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 286\u001b[39m \u001b[43m \u001b[49m\u001b[43mnoise_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 287\u001b[39m \u001b[43m \u001b[49m\u001b[43mnode_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 288\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 289\u001b[39m \u001b[43m \u001b[49m\u001b[43mauto_assign_quality\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 290\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_is_categorical\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 291\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 293\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attribution_func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 294\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m target_is_categorical:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/dowhy/gcm/influence.py:478\u001b[39m, in \u001b[36m_get_icc_noise_function\u001b[39m\u001b[34m(causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical)\u001b[39m\n\u001b[32m 475\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m prediction_model.predict\n\u001b[32m 477\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m prediction_model == \u001b[33m\"\u001b[39m\u001b[33mapprox\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m478\u001b[39m prediction_model = \u001b[43mauto\u001b[49m\u001b[43m.\u001b[49m\u001b[43mselect_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnoise_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauto_assign_quality\u001b[49m\u001b[43m)\u001b[49m[\u001b[32m0\u001b[39m]\n\u001b[32m 479\u001b[39m prediction_model.fit(noise_samples, target_samples)\n\u001b[32m 481\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m target_is_categorical:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/dowhy/gcm/auto.py:457\u001b[39m, in \u001b[36mselect_model\u001b[39m\u001b[34m(X, Y, model_selection_quality)\u001b[39m\n\u001b[32m 455\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m best_model(), model_performances\n\u001b[32m 456\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m457\u001b[39m best_model, model_performances = \u001b[43mfind_best_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 458\u001b[39m \u001b[43m \u001b[49m\u001b[43mlist_of_regressor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_selection_splits\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel_selection_splits\u001b[49m\n\u001b[32m 459\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 460\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m best_model(), model_performances\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/dowhy/gcm/auto.py:585\u001b[39m, in \u001b[36mfind_best_model\u001b[39m\u001b[34m(prediction_model_factories, X, Y, metric, max_samples_per_split, model_selection_splits, n_jobs)\u001b[39m\n\u001b[32m 582\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mfloat\u001b[39m(np.mean(average_result))\n\u001b[32m 584\u001b[39m random_seeds = np.random.randint(np.iinfo(np.int32).max, size=\u001b[38;5;28mlen\u001b[39m(prediction_model_factories))\n\u001b[32m--> \u001b[39m\u001b[32m585\u001b[39m average_metric_scores = \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 586\u001b[39m \u001b[43m \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimate_average_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprediction_model_factory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 587\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mprediction_model_factory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprediction_model_factories\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_seeds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 588\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 589\u001b[39m sorted_results = \u001b[38;5;28msorted\u001b[39m(\n\u001b[32m 590\u001b[39m \u001b[38;5;28mzip\u001b[39m(prediction_model_factories, average_metric_scores, [metric_name] * \u001b[38;5;28mlen\u001b[39m(prediction_model_factories)),\n\u001b[32m 591\u001b[39m key=\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[32m1\u001b[39m],\n\u001b[32m 592\u001b[39m )\n\u001b[32m 594\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m sorted_results[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m], sorted_results\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/joblib/parallel.py:2072\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m 2066\u001b[39m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[32m 2067\u001b[39m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[32m 2068\u001b[39m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[32m 2069\u001b[39m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[32m 2070\u001b[39m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[32m-> \u001b[39m\u001b[32m2072\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/joblib/parallel.py:1682\u001b[39m, in \u001b[36mParallel._get_outputs\u001b[39m\u001b[34m(self, iterator, pre_dispatch)\u001b[39m\n\u001b[32m 1679\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m 1681\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.retrieval_context():\n\u001b[32m-> \u001b[39m\u001b[32m1682\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._retrieve()\n\u001b[32m 1684\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[32m 1685\u001b[39m \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[32m 1686\u001b[39m \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[32m 1687\u001b[39m \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[32m 1688\u001b[39m \u001b[38;5;28mself\u001b[39m._exception = \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/Library/Python/3.12/lib/python/site-packages/joblib/parallel.py:1800\u001b[39m, in \u001b[36mParallel._retrieve\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1789\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_ordered:\n\u001b[32m 1790\u001b[39m \u001b[38;5;66;03m# Case ordered: wait for completion (or error) of the next job\u001b[39;00m\n\u001b[32m 1791\u001b[39m \u001b[38;5;66;03m# that have been dispatched and not retrieved yet. If no job\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1795\u001b[39m \u001b[38;5;66;03m# control only have to be done on the amount of time the next\u001b[39;00m\n\u001b[32m 1796\u001b[39m \u001b[38;5;66;03m# dispatched job is pending.\u001b[39;00m\n\u001b[32m 1797\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (nb_jobs == \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m 1798\u001b[39m \u001b[38;5;28mself\u001b[39m._jobs[\u001b[32m0\u001b[39m].get_status(timeout=\u001b[38;5;28mself\u001b[39m.timeout) == TASK_PENDING\n\u001b[32m 1799\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1800\u001b[39m \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 1801\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 1803\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m nb_jobs == \u001b[32m0\u001b[39m:\n\u001b[32m 1804\u001b[39m \u001b[38;5;66;03m# Case unordered: jobs are added to the list of jobs to\u001b[39;00m\n\u001b[32m 1805\u001b[39m \u001b[38;5;66;03m# retrieve `self._jobs` only once completed or in error, which\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1811\u001b[39m \u001b[38;5;66;03m# timeouts before any other dispatched job has completed and\u001b[39;00m\n\u001b[32m 1812\u001b[39m \u001b[38;5;66;03m# been added to `self._jobs` to be retrieved.\u001b[39;00m\n", - "\u001b[31mKeyboardInterrupt\u001b[39m: " + "name": "stderr", + "output_type": "stream", + "text": [ + "Evaluating set functions...: 100%|██████████| 92/92 [00:22<00:00, 4.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intrinsic causal influence on model_prediction:\n", + " model_prediction 0.027554 ← demographic\n", + " image_brightness 0.008629 ← SPURIOUS\n", + " scanner_acquisition_group 0.004099 ← SPURIOUS\n", + " opacity_score 0.001220 ← legitimate\n", + " patient_age 0.000493 ← demographic\n", + " patient_sex 0.000022 ← demographic\n", + " infiltration -0.000005 ← legitimate\n" ] } ], @@ -682,12 +686,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -700,8 +704,8 @@ "output_type": "stream", "text": [ "\n", - "Legitimate signal share : 2.5%\n", - "Spurious signal share : 29.4%\n" + "Legitimate signal share : 2.9%\n", + "Spurious signal share : 30.3%\n" ] } ], @@ -752,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -762,15 +766,15 @@ "Computing arrow strengths...\n", "\n", "Arrow strengths (KL divergence if edge removed):\n", - " image_brightness → model_prediction 0.013849 ← SPURIOUS\n", - " opacity_score → model_prediction 0.002527 ← legitimate\n", - " patient_age → model_prediction 0.001657 ← demographic\n", - " patient_sex → model_prediction 0.000709 ← demographic\n" + " image_brightness → model_prediction 0.012537 ← SPURIOUS\n", + " opacity_score → model_prediction 0.003017 ← legitimate\n", + " patient_age → model_prediction 0.001931 ← demographic\n", + " patient_sex → model_prediction 0.000828 ← demographic\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -829,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -845,9 +849,9 @@ "3.0 106.95\n", "Name: image_brightness, dtype: float64\n", "\n", - "Observational mean prediction : 0.4360\n", - "Interventional mean prediction : 0.5438\n", - "Mean shift : +0.1078\n" + "Observational mean prediction : 0.4411\n", + "Interventional mean prediction : 0.5544\n", + "Mean shift : +0.1132\n" ] } ], @@ -877,12 +881,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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gbtLS0qxq1aquy/DAgQPdmINHHnlknrMi16hRI/Bo0qRJsR4HAABAwo4h+Prrrwd+nzNnjgu2fAr0VHvbvHnz4i0hAAAAEk61atVs+fLltm3bNhdjagxC9URRd+KcjB8/3q3jUwtBkoIAAADFkBAcPHiw+5mUlGTDhw8Pea18+fIuGXjnnXcWZJMAAAAow+rWrWspKSm2YcOGkOV63qBBg1zfp27FrVu3dr/vt99+tmLFCtcKMLeEYGpqqnsAAACgmBOCWVlZ7meLFi1s8eLFLsADgER2QuYa223JVsH+/f8x5rXqYp6GfEhJiXZJAMQ4JeyuuOIK1zpv48aNbiy/YDkNH5MTdfnt1q2b245fuayYUs9HjRoVcXn0Hg1PAwAAgFJOCPpWr15dDLsGgPh3YFb2CZZiWv1m0S4BgDhx1lln2Zo1a+y6666zhg0buh4ihaWuvOpdcsABB1j37t1t+vTptn37djfrsAwbNswaN27sWgCKfmpdzTCsJODbb79tTz75pD3wwAPFdnwAAACJrFAJQVGtrl9j7Lcc9M2aNas4ygYAAIAoWbhwoX388ceuu25RnXLKKbZp0ya7/vrr3UQi2ua7774bmGhEiUd1EfYpWXjxxRfbb7/9ZpUqVbL27dvbU0895bYDAACAKCUEJ02aZDfeeKOruS1qjTEAAABijybkCO8mXBTqHpxbF+H58+eHPL/55pvdAwAAADGUEJw5c6Y99thjduaZZxZ/iQAgjmywipaZlGQpnmf1bZfFvB1bzPQFXxU5latHuzQAYpi69V599dX24IMPuonjAAAAkOAJwd27d1uvXr2KvzQAEGfuLr+PbU5KtZpeuk3es8xi3vKPLCl9p3mplcx6DYp2aQDEMHXP3bFjhxvHr3Llyla+fPmQ1//++++olQ0AAABRSAied9559swzz7hBpgEAAFA2WwgCAACgbCpUQnDXrl320EMP2QcffGBdunTJVmM8bdq04iofAAAAokCzAgMAAKBsKlRC8Ouvvw7MOPftt9+GvMYEIwAAAGVDZmamvfrqq7ZixQr3vGPHjnbcccdZSkpKtIsGAACA0k4Izps3ryj7BADEuF5zZua7TtrXlUqlLACi46effrJjjjnGfv/9d2vXrp1bNmXKFDf78FtvveXGFgQAAEB8So52AQAAABB7Ro8e7ZJ+a9eutS+//NI91qxZYy1atHCvAQAAIMFaCPbt2zfPrsEffvhhUcoEAACAKPvoo49s0aJFVrt27cCyOnXq2K233moHH3xwVMsGAACAKCQE/fEDfXv27LHly5e78QQZgBoAACD+paam2tatW7Mt37Ztm1WoUCEqZQIAAEAUE4J33XVXjstvuOEGFyQCAAAgvh177LF2/vnn2yOPPGLdu3d3yz7//HO78MIL3cQiAAAAiF/FOobgGWecYbNmzSrOTQIAACAK7rnnHjeGYM+ePa1ixYruoa7CrVu3trvvvjvaxQMAAEBptxDMzWeffeaCxUhpprqXX37ZfvjhB6tUqZL16tXLbrvttsBMdrJr1y67/PLLbfbs2Zaenm4DBgyw+++/3+rXr1+cRQeAQrlqz7eWZUmWbJ7FhW79zPM8szzGgQUAqVmzpr322mv2448/ulhN9tlnH5cQBAAAQAImBE888cSQ5/pyuW7dOluyZIldd911BRqseuTIkXbggQdaRkaGTZgwwfr372/ff/+9ValSxa1z2WWX2VtvvWUvvPCC1ahRw0aNGuX2/8knnxSm6ABQrGrYHosrqZWiXQIAcaZNmzbuAQAAgARPCCoxFyw5Odm16rvxxhtdQi9S7777bsjzxx57zOrVq2dLly613r17W1pamhu35plnnrHDDz/crfPoo4+62mnNenfQQQdl26ZaEerh27JlSyGOEAAAIPGMHTvWbrrpJlcxq9/zMm3atFIrFwAAAGIgIaikXElQAlBq167tfioxqBmM+/XrF1inffv21rRpU9c9OaeEoLohT5o0qUTKBwAAUJYtW7bMxV7+7wAAACibijSGoBJ2K1ascL937NjRunbtWuhtZWVl2aWXXuoGq+7UqZNbtn79eqtQoYIbwyaYxg/UazkZP358SI22Wgg2adKk0OUCgLwsTK5n6ZZsqZZlh2RttJj3xyqzjAyzcuXMGrWKdmkAxJh58+bl+DsAAADKlkIlBDdu3GinnnqqzZ8/P5Cs27x5s/Xt29dN/rHXXnsVeJsaS/Dbb7+1hQsXWlGkpqa6BwCUhrdTGtvmpFSr6aXHR0Lwl+8tKX2neRpLkIQggDycc845bjbhatWqhSzfvn27XXLJJTZr1qyolQ0AAABFk1yYNykI3Lp1q3333Xf2999/u4eSeWqNN3r06AJvTxOFvPnmm64meu+99w4sb9Cgge3evdslG4Nt2LDBvQYAAICS8fjjj9vOnTuzLdeyJ554IiplAgAAQBRbCGoykA8++MBN7uHr0KGD3XfffQWaVESzEyu5+Morr7jWhi1atAh5vVu3bla+fHmbO3euDRkyxC1buXKlrVmzxnr27FmYogMAACAPquBVjKaHKoArVqwYeC0zM9PefvttNwkcAAAAEiwhqPH+lKgLp2V6rSDdhDWD8Guvvea6o/jjAmoW40qVKrmf5557rhsTUBONVK9e3SUQlQzMaUIRAAAAFI2Gg0lKSnKPtm3bZntdy5nADQAAIAETgocffriNGTPGnn32WWvUqJFb9vvvv9tll11mRxxxRMTbeeCBB9zPww47LNssxmeddZb7/a677rLk5GTXQjA9Pd0GDBhg999/f2GKDQAAgHxoCBe1DlS899JLL7lKWZ8me2vWrFkg/gMAAEACJQRnzJhhxx13nDVv3jwwg+/atWvd7MBPPfVUxNtRsJkfdVNRV2Q9AAAAULL69Onjfq5evdqaNm3qWgQCAACgbClUQlBJwC+//NKNI/jDDz+4ZRpPsF+/fsVdPgAAAETBhx9+aFWrVrWTTjopZPkLL7xgO3bssOHDh0etbAAAACjFWYYVGGryEA02rdriI4880o3pp8eBBx5oHTt2tI8//riIRQIAAEC0TZkyxerWrZttuSYUmTx5clTKBAAAgCi0EJw+fbqNGDHCTe4RThOAXHDBBTZt2jQ79NBDi6l4AICyZNm6nXmvcM2N2RZ1bVgpx1VrjBpXXMUCkIM1a9ZYixYtsi3XGIJ6DQAAAAnSQvCrr76yo446KtfX+/fvb0uXLi2OcgFAXKjn7bKGWTvcz7hQqZp5lau7nwCQF7UE/Prrr3OMB+vUqROVMgEAACAKLQQ3bNhg5cuXz31j5crZpk2biqNcABAXLs1YYXGla+is7gCQm6FDh9ro0aOtWrVq1rt3b7fso48+sjFjxtipp54a7eIBAACgtBKCjRs3tm+//dZat26d4+uqRW7YsGFRygMAAIAYcNNNN9kvv/xiRxxxhKv0laysLBs2bBhjCAIAACRSQvCYY46x6667znUbrlixYshrO3futIkTJ9qxxx5b3GUEAABAKatQoYI999xzLjGobsKVKlWyzp07uzEEAQAAkEAJwWuvvdZefvlla9u2rY0aNcratWvnlv/www923333WWZmpl1zzTUlVVYAAACUMsV9egAAACBBE4L169e3Tz/91C666CIbP368eZ7nliclJdmAAQNcUlDrAECieDSltW1LKmdVvQw7O/Mni3nfLzLbnW5WIdWsw0HRLg2AGDN27FjXIrBKlSru97xMmzat1MoFAACAKCYERd1E3n77bfvnn3/sp59+cknBNm3aWK1atYq5aAAQ+35Mrmabk1Ktppdulmmxb/MmS0rfaV5qpWiXBEAMWrZsme3Zsyfwe25UGQwAAIAESgj6lAA88MADi7c0AAAAiJp58+bl+DsAAADKlkInBAHEvskLNka7CAAAAAAAIMaQEAQAAIBz4oknRryuJpoDAABAfEqOdgEAAAAQG2rUqBF4VK9e3ebOnWtLliwJvL506VK3TK8DAAAgftFCEAAAAM6jjz4a+P2qq66yk08+2WbOnGkpKSluWWZmpl188cUuWQgAAID4RQtBAAAAZDNr1iy74oorAslA0e9jx451rwEAACB+kRAEAABANhkZGfbDDz9kW65lWVlZUSkTAAAAigddhgEAAJDN2Wefbeeee66tWrXKunfv7pZ9/vnnduutt7rXAAAAEL9ICAJAERycudF2JpWzSl6GxYWGLc3L2GNWrny0SwIgxt1xxx3WoEEDu/POO23dunVuWcOGDW3cuHF2+eWXR7t4AAAAKAISggBQBAOzfre40qJjtEsAIE4kJyfblVde6R5btmxxy5hMBAAAoGwgIQgAiEtpM6ZGtF6NUeNKvCxAWR5HcP78+a7b8GmnneaW/fHHHy4xWLVq1WgXDwAAAIVEQhAAAADZ/Prrr3bUUUfZmjVrLD093Y488kirVq2a3Xbbbe75zJkzo11EAAAAFBKzDAMAACCbMWPG2AEHHGD//POPVapUKbD8hBNOsLlz50a1bAAAACgaWggCQBFMKN/VNielWk0v3SbvWWYx79M3LCl9p3mplcx6DYp2aQDEsI8//tg+/fRTq1ChQsjy5s2b2++/x9n4qQAAAAhBC0EAAABkk5WVZZmZmdmW//bbb67rMAAAAOIXCUEAAABk079/f5s+fXrgeVJSkm3bts0mTpxoxxxzTFTLBgAAgKKhyzAAAACyueOOO9ykIh06dLBdu3a5WYZ//PFHq1u3rj377LPRLh4AAACKgIQgAAAAsmnSpIl99dVX9txzz7mfah147rnn2umnnx4yyQgAAADiDwlBAAAAhNizZ4+1b9/e3nzzTZcA1AMAAABlB2MIAgAAIET58uVdN2EAAACUTSQEAQAAkM3IkSPttttus4yMjGgXBQAAAMWMLsMAAADIZvHixTZ37lx77733rHPnzlalSpWQ119++eWolQ0AAABFQ0IQAAAA2dSsWdOGDBkS7WIAAACgBJAQBIAiOCtjlWVYkpU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", 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