diff --git a/dowhy/causal_estimators/distance_matching_estimator.py b/dowhy/causal_estimators/distance_matching_estimator.py
index b0f2e3430f..b8c61c475f 100644
--- a/dowhy/causal_estimators/distance_matching_estimator.py
+++ b/dowhy/causal_estimators/distance_matching_estimator.py
@@ -94,6 +94,7 @@ def __init__(
         # that will be passed to sklearn nearestneighbors
         self.distance_metric_params = {}
         for param_name in self.Valid_Dist_Metric_Params:
+            # Pull values from kwargs directly since the base class discards them.
             param_val = kwargs.get(param_name, None)
             if param_val is not None:
                 self.distance_metric_params[param_name] = param_val
@@ -200,7 +201,7 @@ 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 if self.distance_metric_params else None,
                 ).fit(control[self._observed_common_causes.columns].values)
                 distances, indices = control_neighbors.kneighbors(treated[self._observed_common_causes.columns].values)
                 self.logger.debug("distances:")
@@ -238,7 +239,7 @@ 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 if self.distance_metric_params else None,
                     ).fit(control[self._observed_common_causes.columns].values)
                     distances, indices = control_neighbors.kneighbors(
                         treated[self._observed_common_causes.columns].values
@@ -267,7 +268,7 @@ 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 if self.distance_metric_params else None,
             ).fit(treated[self._observed_common_causes.columns].values)
             distances, indices = treated_neighbors.kneighbors(control[self._observed_common_causes.columns].values)
 
diff --git a/tests/causal_estimators/test_distance_matching_estimator.py b/tests/causal_estimators/test_distance_matching_estimator.py
new file mode 100644
index 0000000000..4a12294689
--- /dev/null
+++ b/tests/causal_estimators/test_distance_matching_estimator.py
@@ -0,0 +1,102 @@
+import numpy as np
+import pytest
+
+from dowhy.causal_estimators.distance_matching_estimator import DistanceMatchingEstimator
+
+from .base import SimpleEstimator
+
+
+@pytest.mark.usefixtures("fixed_seed")
+class TestDistanceMatchingEstimator:
+    @pytest.mark.parametrize(
+        [
+            "error_tolerance",
+            "Estimator",
+            "num_common_causes",
+            "num_instruments",
+            "num_effect_modifiers",
+            "num_treatments",
+            "treatment_is_binary",
+            "outcome_is_binary",
+            "identifier_method",
+        ],
+        [
+            (
+                0.3,
+                DistanceMatchingEstimator,
+                [1, 2],
+                [0],
+                [0],
+                [1],
+                [True],
+                [False],
+                "backdoor",
+            ),
+        ],
+    )
+    def test_average_treatment_effect(
+        self,
+        error_tolerance,
+        Estimator,
+        num_common_causes,
+        num_instruments,
+        num_effect_modifiers,
+        num_treatments,
+        treatment_is_binary,
+        outcome_is_binary,
+        identifier_method,
+    ):
+        estimator_tester = SimpleEstimator(error_tolerance, Estimator, identifier_method=identifier_method)
+        estimator_tester.average_treatment_effect_testsuite(
+            num_common_causes=num_common_causes,
+            num_instruments=num_instruments,
+            num_effect_modifiers=num_effect_modifiers,
+            num_treatments=num_treatments,
+            treatment_is_binary=treatment_is_binary,
+            outcome_is_binary=outcome_is_binary,
+        )
+
+    def test_distance_metric_params_passed_to_estimator(self):
+        """Regression test for https://github.com/py-why/dowhy/issues/1390.
+
+        distance_metric_params such as V (for Mahalanobis) must be forwarded
+        from method_params to NearestNeighbors, not silently dropped.
+        """
+        import dowhy.datasets
+        from dowhy import EstimandType, identify_effect_auto
+        from dowhy.graph import build_graph_from_str
+
+        data = dowhy.datasets.linear_dataset(
+            beta=10,
+            num_common_causes=2,
+            num_instruments=0,
+            num_effect_modifiers=0,
+            num_treatments=1,
+            num_samples=500,
+            treatment_is_binary=True,
+        )
+        graph = build_graph_from_str(data["gml_graph"])
+        observed_nodes = list(data["df"].columns)
+        identified_estimand = identify_effect_auto(
+            graph,
+            data["treatment_name"],
+            data["outcome_name"],
+            observed_nodes,
+            EstimandType.NONPARAMETRIC_ATE,
+        )
+        common_causes = data["df"][data["common_causes_names"]].values
+        V = np.cov(common_causes.T)
+
+        estimator = DistanceMatchingEstimator(
+            identified_estimand=identified_estimand,
+            distance_metric="mahalanobis",
+            V=V,
+        )
+        assert estimator.distance_metric_params == {"V": V}, (
+            "distance_metric_params should capture V from kwargs"
+        )
+
+        # Also verify that fit + estimate_effect works end-to-end without error.
+        estimator.fit(data["df"])
+        estimate = estimator.estimate_effect(data["df"], target_units="att")
+        assert estimate.value is not None