Skip to content

fix(estimator): pass distance metric parameters as metric_params to NearestNeighbors #1563

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
TasfinMahmud wants to merge 2 commits into
base: main
Choose a base branch
from
+26 −3
Open

fix(estimator): pass distance metric parameters as metric_params to NearestNeighbors #1563

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f62ecb0
fix(estimator): pass distance metric parameters as metric_params to N…
TasfinMahmud Jun 4, 2026
77bdf02
fixed format
amit-sharma Jun 6, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions dowhy/causal_estimators/distance_matching_estimator.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -217,7 +217,7 @@ def estimate_effect(
n_neighbors=self.num_matches_per_unit,
metric=self.distance_metric,
algorithm="ball_tree",
**self.distance_metric_params,
metric_params=self.distance_metric_params,
).fit(control[self._observed_common_causes.columns].values)
distances, indices = control_neighbors.kneighbors(treated[self._observed_common_causes.columns].values)
self.logger.debug("distances:")
Expand Down Expand Up @@ -257,7 +257,7 @@ def estimate_effect(
n_neighbors=self.num_matches_per_unit,
metric=self.distance_metric,
algorithm="ball_tree",
**self.distance_metric_params,
metric_params=self.distance_metric_params,
).fit(group_control[self._observed_common_causes.columns].values)
distances, indices = control_neighbors.kneighbors(
group_treated[self._observed_common_causes.columns].values
Expand Down Expand Up @@ -293,7 +293,7 @@ def estimate_effect(
n_neighbors=self.num_matches_per_unit,
metric=self.distance_metric,
algorithm="ball_tree",
**self.distance_metric_params,
metric_params=self.distance_metric_params,
).fit(treated[self._observed_common_causes.columns].values)
distances, indices = treated_neighbors.kneighbors(control[self._observed_common_causes.columns].values)

Expand Down
23 changes: 23 additions & 0 deletions tests/causal_estimators/test_distance_matching_estimator.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -149,3 +149,26 @@ def test_average_treatment_effect_via_simple_estimator(self):
test_significance=[False],
method_params={"num_simulations": 5, "num_null_simulations": 5},
)

def test_distance_matching_with_mahalanobis_and_v_param(self, binary_treatment_dataset):
"""Regression test for issue #1390: ensure V param is correctly passed as metric_params to NearestNeighbors."""
data = binary_treatment_dataset
model = CausalModel(data=data, treatment="v0", outcome="y", graph=GML_SINGLE_CAUSE)
estimand = model.identify_effect(proceed_when_unidentifiable=True)

# Calculate covariance matrix for W to use as V parameter
X = data[["W"]].values
V_matrix = np.cov(X.T)
if V_matrix.ndim == 0:
V_matrix = np.array([[V_matrix]])

estimate = model.estimate_effect(
estimand,
method_name="backdoor.distance_matching",
target_units="att",
method_params={
"distance_metric": "mahalanobis",
"V": V_matrix,
},
)
assert np.isfinite(estimate.value), "Estimate with Mahalanobis and V param should be finite."
Loading