diff --git a/causalml/inference/meta/explainer.py b/causalml/inference/meta/explainer.py
index b92fee86..b156b027 100644
--- a/causalml/inference/meta/explainer.py
+++ b/causalml/inference/meta/explainer.py
@@ -47,7 +47,7 @@ def __init__(
             X (np.matrix): a feature matrix
             tau (np.array): a treatment effect vector (estimated/actual)
             classes (dict): a mapping of treatment names to indices (used for indexing tau array)
-            model_tau (sklearn/lightgbm/xgboost model object): a model object
+            model_tau (sklearn/lightgbm/xgboost/catboost model object): a model object
             features (np.array): list/array of feature names. If None, an enumerated list will be used.
             normalize (bool): normalize by sum of importances if method=auto (defaults to True)
             test_size (float/int): if float, represents the proportion of the dataset to include in the test split.
@@ -84,7 +84,7 @@ def check_conditions(self):
         Checks for multiple conditions:
             - method is valid
             - X, tau, and classes are specified
-            - model_tau has feature_importances_ attribute after fitting
+            - model_tau has feature_importances_ after fitting
         """
         assert self.method in VALID_METHODS, "Current supported methods: {}".format(
             ", ".join(VALID_METHODS)
@@ -97,7 +97,8 @@ def check_conditions(self):
         model_test = deepcopy(self.model_tau)
         model_test.fit(
             [[0], [1]], [0, 1]
-        )  # Fit w/ dummy data to check for feature_importances_ below
+        )  # Fit w/ dummy data to ensure feature importances are available
+
         assert hasattr(
             model_test, "feature_importances_"
         ), "model_tau must have the feature_importances_ method (after fitting)"
diff --git a/tests/test_meta_learners.py b/tests/test_meta_learners.py
index 2efb77b0..ad0dcd7b 100644
--- a/tests/test_meta_learners.py
+++ b/tests/test_meta_learners.py
@@ -36,6 +36,7 @@
 from causalml.inference.meta import BaseDRLearner
 from causalml.inference.meta import BaseDRRegressor
 from causalml.inference.meta import BaseDRClassifier
+from causalml.inference.meta.explainer import Explainer
 from causalml.metrics import ape, auuc_score
 
 from .const import RANDOM_SEED, N_SAMPLE, ERROR_THRESHOLD, CONTROL_NAME, CONVERSION
@@ -640,6 +641,34 @@ def test_BaseRRegressor(generate_regression_data):
     assert auuc["cate_p"] > 0.5
 
 
+def test_explainer_auto_importance_catboost(generate_regression_data):
+    catboost = pytest.importorskip("catboost")
+
+    y, X, treatment, tau, b, e = generate_regression_data()
+
+    model_tau = catboost.CatBoostRegressor(
+        iterations=10,
+        verbose=False,
+        random_seed=RANDOM_SEED,
+    )
+
+    explainer = Explainer(
+        method="auto",
+        control_name=CONTROL_NAME,
+        X=X,
+        tau=tau,
+        classes={CONTROL_NAME: 0},
+        model_tau=model_tau,
+    )
+
+    importance = explainer.get_importance()
+
+    assert len(importance) == 1
+    assert CONTROL_NAME in importance
+    assert isinstance(importance[CONTROL_NAME], pd.Series)
+    assert len(importance[CONTROL_NAME]) == X.shape[1]
+
+
 def test_BaseRLearner_without_p(generate_regression_data):
     y, X, treatment, tau, b, e = generate_regression_data()