diff --git a/dice_ml/explainer_interfaces/explainer_base.py b/dice_ml/explainer_interfaces/explainer_base.py
index ad231e69..af5f77ce 100644
--- a/dice_ml/explainer_interfaces/explainer_base.py
+++ b/dice_ml/explainer_interfaces/explainer_base.py
@@ -5,6 +5,7 @@
 import pickle
 from abc import ABC, abstractmethod
 from collections.abc import Iterable
+from typing import Any, List
 
 import numpy as np
 import pandas as pd
@@ -47,6 +48,24 @@ def __init__(self, data_interface, model_interface=None):
         # self.cont_precisions = \
         #   [self.data_interface.get_decimal_precisions()[ix] for ix in self.encoded_continuous_feature_indexes]
 
+    def _find_features_having_missing_values(
+            self, data: Any) -> List[str]:
+        """Return list of features which have missing values.
+
+        :param data: The dataset to check.
+        :type data: Any
+        :return: List of feature names which have missing values.
+        :rtype: List[str]
+        """
+        if not isinstance(data, pd.DataFrame):
+            return []
+
+        list_of_feature_having_missing_values = []
+        for feature in data.columns.tolist():
+            if np.any(data[feature].isnull()):
+                list_of_feature_having_missing_values.append(feature)
+        return list_of_feature_having_missing_values
+
     def _validate_counterfactual_configuration(
             self, query_instances, total_CFs,
             desired_class="opposite", desired_range=None,
@@ -54,6 +73,12 @@ def _validate_counterfactual_configuration(
             stopping_threshold=0.5, posthoc_sparsity_param=0.1,
             posthoc_sparsity_algorithm="linear", verbose=False, **kwargs):
 
+        if len(self._find_features_having_missing_values(query_instances)) > 0:
+            raise UserConfigValidationException(
+                "The query instance(s) should not have any missing values. "
+                "Please impute the missing values and try again."
+            )
+
         if total_CFs <= 0:
             raise UserConfigValidationException(
                 "The number of counterfactuals generated per query instance (total_CFs) should be a positive integer.")
diff --git a/tests/test_dice_interface/test_explainer_base.py b/tests/test_dice_interface/test_explainer_base.py
index f898ddde..4325bd16 100644
--- a/tests/test_dice_interface/test_explainer_base.py
+++ b/tests/test_dice_interface/test_explainer_base.py
@@ -1,3 +1,6 @@
+import re
+
+import numpy as np
 import pandas as pd
 import pytest
 from rai_test_utils.datasets.tabular import create_housing_data
@@ -501,6 +504,27 @@ def test_generate_counterfactuals_user_config_validations(
             method=method)
 
         explainer_function = getattr(exp, explainer_function)
+
+        regex_pattern = re.escape(
+            'The query instance(s) should not have any missing values. '
+            'Please impute the missing values and try again.')
+
+        query_instances_missing_values_numerical = pd.DataFrame({'Categorical': ['a'], 'Numerical': [np.nan]})
+        with pytest.raises(
+                UserConfigValidationException,
+                match=regex_pattern):
+            explainer_function(
+                query_instances=query_instances_missing_values_numerical, desired_class='opposite',
+                total_CFs=10)
+
+        query_instances_missing_values_categorical = pd.DataFrame({'Categorical': [np.nan], 'Numerical': [1]})
+        with pytest.raises(
+                UserConfigValidationException,
+                match=regex_pattern):
+            explainer_function(
+                query_instances=query_instances_missing_values_categorical, desired_class='opposite',
+                total_CFs=10)
+
         with pytest.raises(
                 UserConfigValidationException,
                 match=r"The number of counterfactuals generated per query instance \(total_CFs\) "