diff --git a/dowhy/gcm/anomaly.py b/dowhy/gcm/anomaly.py
index 46f67e6de..2026787eb 100644
--- a/dowhy/gcm/anomaly.py
+++ b/dowhy/gcm/anomaly.py
@@ -94,7 +94,7 @@ def attribute_anomalies(
     anomaly_samples: pd.DataFrame,
     anomaly_scorer: Optional[AnomalyScorer] = None,
     attribute_mean_deviation: bool = False,
-    num_distribution_samples: int = 1500,
+    num_distribution_samples: int = 3000,
     shapley_config: Optional[ShapleyConfig] = None,
 ) -> Dict[Any, np.ndarray]:
     """Estimates the contributions of upstream nodes to the anomaly score of the target_node for each sample in
diff --git a/dowhy/gcm/shapley.py b/dowhy/gcm/shapley.py
index 29c660245..0670a1e98 100644
--- a/dowhy/gcm/shapley.py
+++ b/dowhy/gcm/shapley.py
@@ -44,7 +44,7 @@ class ShapleyConfig:
     def __init__(
         self,
         approximation_method: ShapleyApproximationMethods = ShapleyApproximationMethods.AUTO,
-        num_permutations: int = 2000,
+        num_permutations: int = 25,
         num_subset_samples: int = 5000,
         min_percentage_change_threshold: float = 0.05,
         n_jobs: Optional[int] = None,
@@ -110,7 +110,7 @@ def estimate_shapley_values(
         if num_players <= 5:
             approximation_method = ShapleyApproximationMethods.EXACT
         else:
-            approximation_method = ShapleyApproximationMethods.EARLY_STOPPING
+            approximation_method = ShapleyApproximationMethods.PERMUTATION
 
     if approximation_method == ShapleyApproximationMethods.EXACT:
         return _estimate_shapley_values_exact(set_func=set_func, num_players=num_players, n_jobs=shapley_config.n_jobs)
@@ -369,7 +369,7 @@ def _approximate_shapley_values_via_early_stopping(
                         permutation, evaluated_subsets, full_subset_result, empty_subset_result
                     )
 
-            if run_counter > max_num_permutations:
+            if run_counter * num_permutations_per_run > max_num_permutations:
                 break
 
             new_shap_proxy = np.array(shapley_values)
@@ -579,12 +579,15 @@ def parallel_job(input_subset: Tuple[int], parallel_random_seed: int) -> Union[f
 
         return set_func(np.array(input_subset))
 
+    if isinstance(evaluation_subsets, set):
+        evaluation_subsets = list(evaluation_subsets)
+
     random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(evaluation_subsets))
     subset_results = parallel_context(
-        delayed(parallel_job)(subset_to_evaluate, int(random_seed))
-        for subset_to_evaluate, random_seed in tqdm(
-            zip(evaluation_subsets, random_seeds),
-            desc="Evaluate set function",
+        delayed(parallel_job)(evaluation_subsets[i], int(random_seeds[i]))
+        for i in tqdm(
+            range(len(evaluation_subsets)),
+            desc="Evaluating set functions...",
             position=0,
             leave=True,
             disable=not config.show_progress_bars or not show_progressbar,