diff --git a/dowhy/utils/cit.py b/dowhy/utils/cit.py
index e7ec6408aa..74dd1a5b25 100644
--- a/dowhy/utils/cit.py
+++ b/dowhy/utils/cit.py
@@ -139,14 +139,16 @@ def conditional_MI(data=None, x=None, y=None, z=None):
     :param x,y,z : column names from dataset
     :returns : conditional mutual information between X and Y given Z
     """
-    X = data[list(x)].astype(int)
-    Y = data[list(y)].astype(int)
-    t = list(z)
-    Z = data[t].astype(int)
-    Z = Z.values.tolist()
+    x_cols = [x] if isinstance(x, str) else list(x)
+    y_cols = [y] if isinstance(y, str) else list(y)
+    t = list(z) if not isinstance(z, list) else z
+    # Use squeeze() to convert single-column DataFrames to Series so that
+    # iterating yields row values instead of column labels.
+    X = data[x_cols].astype(int).squeeze(axis=1)
+    Y = data[y_cols].astype(int).squeeze(axis=1)
     Z = list(data[t].itertuples(index=False, name=None))
-    Hxz = entropy(map(lambda x: "%s/%s" % x, zip(X, Z)))  # Finding Joint entropy of X and Z
-    Hyz = entropy(map(lambda x: "%s/%s" % x, zip(Y, Z)))  # Finding Joint entropy of Y and Z
+    Hxz = entropy(map(lambda v: "%s/%s" % v, zip(X, Z)))  # Finding Joint entropy of X and Z
+    Hyz = entropy(map(lambda v: "%s/%s" % v, zip(Y, Z)))  # Finding Joint entropy of Y and Z
     Hz = entropy(Z)  # Finding Entropy of Z
-    Hxyz = entropy(map(lambda x: "%s/%s/%s" % x, zip(X, Y, Z)))  # Finding Joint Entropy of X, Y and Z
+    Hxyz = entropy(map(lambda v: "%s/%s/%s" % v, zip(X, Y, Z)))  # Finding Joint Entropy of X, Y and Z
     return Hxz + Hyz - Hxyz - Hz
diff --git a/tests/utils/test_cit.py b/tests/utils/test_cit.py
new file mode 100644
index 0000000000..d95c9c9d65
--- /dev/null
+++ b/tests/utils/test_cit.py
@@ -0,0 +1,69 @@
+import numpy as np
+import pandas as pd
+
+from dowhy.utils.cit import conditional_MI
+
+
+class TestConditionalMI:
+    """Tests for the conditional_MI function."""
+
+    def test_multi_char_column_names(self):
+        """Regression test for #949: column names with >1 character were iterated as chars."""
+        rng = np.random.default_rng(42)
+        n = 200
+        # Use multi-character column names that would be broken by list("Foo") -> ['F','o','o']
+        df = pd.DataFrame(
+            {
+                "Foo": rng.integers(0, 3, size=n),
+                "Bar": rng.integers(0, 3, size=n),
+                "Baz": rng.integers(0, 3, size=n),
+            }
+        )
+        # Should not raise KeyError
+        result = conditional_MI(data=df, x="Foo", y="Bar", z=["Baz"])
+        assert isinstance(result, float)
+
+    def test_single_char_column_names(self):
+        """Single-character column names should still work."""
+        rng = np.random.default_rng(0)
+        n = 200
+        df = pd.DataFrame(
+            {
+                "X": rng.integers(0, 2, size=n),
+                "Y": rng.integers(0, 2, size=n),
+                "Z": rng.integers(0, 2, size=n),
+            }
+        )
+        result = conditional_MI(data=df, x="X", y="Y", z=["Z"])
+        assert isinstance(result, float)
+
+    def test_independent_variables_low_cmi(self):
+        """Independent variables should have low conditional mutual information."""
+        rng = np.random.default_rng(7)
+        n = 5000
+        df = pd.DataFrame(
+            {
+                "Alpha": rng.integers(0, 2, size=n),  # independent of Beta
+                "Beta": rng.integers(0, 2, size=n),
+                "Gamma": rng.integers(0, 2, size=n),
+            }
+        )
+        result = conditional_MI(data=df, x="Alpha", y="Beta", z=["Gamma"])
+        # Truly independent variables should yield low CMI
+        assert result < 0.05
+
+    def test_dependent_variables_high_cmi(self):
+        """Fully dependent variables should have high conditional mutual information."""
+        rng = np.random.default_rng(42)
+        n = 1000
+        x_vals = rng.integers(0, 2, size=n)
+        df = pd.DataFrame(
+            {
+                "Foo": x_vals,
+                "Bar": x_vals,  # identical to Foo -> fully dependent
+                "Baz": rng.integers(0, 2, size=n),
+            }
+        )
+        result = conditional_MI(data=df, x="Foo", y="Bar", z=["Baz"])
+        # Fully dependent variables should yield CMI close to 1 bit
+        assert result > 0.5