diff --git a/dowhy/gcm/anomaly_scorers.py b/dowhy/gcm/anomaly_scorers.py
index c5c41f35d8..9a4735fa02 100644
--- a/dowhy/gcm/anomaly_scorers.py
+++ b/dowhy/gcm/anomaly_scorers.py
@@ -43,13 +43,13 @@ def fit(self, X: np.ndarray) -> None:
         if (X.ndim == 2 and X.shape[1] > 1) or X.ndim > 2:
             raise ValueError("The MedianCDFQuantileScorer currently only supports one-dimensional data!")
 
-        self._distribution_samples = X.reshape(-1)
+        self._distribution_samples = X.reshape(-1).astype(float)
 
     def score(self, X: np.ndarray) -> np.ndarray:
         if self._distribution_samples is None:
             raise ValueError("Scorer has not been fitted!")
 
-        X = shape_into_2d(X)
+        X = shape_into_2d(X.astype(float))
 
         equal_samples = np.sum(np.isclose(X, self._distribution_samples, rtol=0, atol=0, equal_nan=True), axis=1)
         greater_samples = np.sum(X > self._distribution_samples, axis=1) + equal_samples / 2
diff --git a/tests/gcm/test_anomaly_scorers.py b/tests/gcm/test_anomaly_scorers.py
index c7f45e01fc..e28fc42c90 100644
--- a/tests/gcm/test_anomaly_scorers.py
+++ b/tests/gcm/test_anomaly_scorers.py
@@ -29,3 +29,14 @@ def test_given_data_with_nans_when_using_median_quantile_scorer_with_nan_support
     assert scorer.score(np.array([1, 4, 8, np.nan])) == approx(
         [-np.log(2 * 0.5 / 10), -np.log(2 * 3.5 / 10), -np.log(2 * 0.5 / 10), -np.log(2 * 1 / 10)]
     )
+
+
+def test_given_numpy_arrays_with_object_type_when_using_median_quantile_scorer_then_does_not_raise_error():
+    training_data = np.array([1, 2, 3, 4, 5, 6, 7, 8, np.nan, np.nan], dtype=object)
+
+    scorer = RescaledMedianCDFQuantileScorer()
+    scorer.fit(training_data)
+
+    assert scorer.score(np.array([1, 4, 8, np.nan], dtype=object)) == approx(
+        [-np.log(2 * 0.5 / 10), -np.log(2 * 3.5 / 10), -np.log(2 * 0.5 / 10), -np.log(2 * 1 / 10)]
+    )