diff --git a/dowhy/gcm/ml/classification.py b/dowhy/gcm/ml/classification.py
index d4eaac39d0..d3b82413a8 100644
--- a/dowhy/gcm/ml/classification.py
+++ b/dowhy/gcm/ml/classification.py
@@ -76,7 +76,7 @@ def create_ada_boost_classifier(**kwargs) -> SklearnClassificationModel:
 
 
 def create_support_vector_classifier(**kwargs) -> SklearnClassificationModel:
-    return SklearnClassificationModel(SVC(**kwargs))
+    return SklearnClassificationModel(SVC(**kwargs, probability=True))
 
 
 def create_knn_classifier(**kwargs) -> SklearnClassificationModel:
diff --git a/tests/gcm/ml/test_classification.py b/tests/gcm/ml/test_classification.py
index 8a1fc5207b..7208512a1c 100644
--- a/tests/gcm/ml/test_classification.py
+++ b/tests/gcm/ml/test_classification.py
@@ -2,6 +2,7 @@
 from flaky import flaky
 
 from dowhy.gcm.ml import create_hist_gradient_boost_classifier, create_polynom_logistic_regression_classifier
+from dowhy.gcm.ml.classification import create_support_vector_classifier
 
 
 @flaky(max_runs=3)
@@ -58,3 +59,9 @@ def _generate_data():
     mdl.fit(X_training, Y_training)
 
     assert np.sum(mdl.predict(X_test).reshape(-1) == Y_test) > 950
+
+
+def test_given_svc_model_then_supports_predict_probabilities():
+    mdl = create_support_vector_classifier()
+    mdl.fit(np.random.normal(0, 1, 100), np.random.choice(2, 100).astype(str))
+    mdl.predict_probabilities(np.random.normal(0, 1, 10))