diff --git a/Murray/main.py b/Murray/main.py
index befb3aa..073963d 100644
--- a/Murray/main.py
+++ b/Murray/main.py
@@ -25,7 +25,7 @@ def is_streamlit_context():
     )
 
 
-def select_treatments(similarity_matrix, treatment_size, excluded_locations):
+def select_treatments(similarity_matrix, treatment_size, excluded_treatment_locations):
     """
     Selects n combinations of treatments based on a similarity DataFrame, excluding certain states
     from the treatment selection but allowing their inclusion in the control.
@@ -34,23 +34,23 @@ def select_treatments(similarity_matrix, treatment_size, excluded_locations):
     Args:
         similarity_matrix (pd.DataFrame): DataFrame containing correlations between locations in a standard matrix format
         treatment_size (int): Number of treatments to select for each combination.
-        excluded_locations (list): List of locations to exclude from the treatment selection.
+        excluded_treatment_locations (list): List of locations to exclude from the treatment selection.
 
 
 
     Returns:
         list: A list of unique combinations, each combination being a list of states.
     """
-    # Filter out empty strings from excluded_locations
-    excluded_locations = [loc for loc in excluded_locations if loc.strip()]
+    # Filter out empty strings from excluded_treatment_locations
+    excluded_treatment_locations = [loc for loc in excluded_treatment_locations if loc.strip()]
     
     logger.debug(
-        f"select_treatments called: treatment_size={treatment_size}, excluded_locations={excluded_locations}"
+        f"select_treatments called: treatment_size={treatment_size}, excluded_treatment_locations={excluded_treatment_locations}"
     )
 
     missing_locations = [
         location
-        for location in excluded_locations
+        for location in excluded_treatment_locations
         if location not in similarity_matrix.index
         or location not in similarity_matrix.columns
     ]
@@ -64,8 +64,8 @@ def select_treatments(similarity_matrix, treatment_size, excluded_locations):
         )
 
     similarity_matrix_filtered = similarity_matrix.loc[
-        ~similarity_matrix.index.isin(excluded_locations),
-        ~similarity_matrix.columns.isin(excluded_locations),
+        ~similarity_matrix.index.isin(excluded_treatment_locations),
+        ~similarity_matrix.columns.isin(excluded_treatment_locations),
     ]
 
     logger.debug(
@@ -88,8 +88,8 @@ def select_treatments(similarity_matrix, treatment_size, excluded_locations):
     n_combinations = max_combinations
     if n_combinations > 5000:
         n_combinations = 5000
-    # if n_combinations > 2000:
-        # n_combinations = 2000
+    #if n_combinations > 2000:
+      #  n_combinations = 2000
 
     logger.debug(f"Generating {n_combinations} combinations")
 
@@ -107,7 +107,7 @@ def select_treatments(similarity_matrix, treatment_size, excluded_locations):
 
 
 def select_controls(
-    correlation_matrix, treatment_group, min_correlation=0.8, fallback_n=1
+    correlation_matrix, treatment_group, min_correlation=0.8, fallback_n=1, excluded_control_locations=None
 ):
     """
     Dynamically selects control group states based on correlation values.
@@ -118,6 +118,7 @@ def select_controls(
         treatment_group (list): List of states in the treatment group.
         min_correlation (float): Minimum correlation threshold to consider a state as part of the control group.
         fallback_n (int): Number of top correlated states to select if no state meets the min_correlation.
+        excluded_control_locations (list): List of states to exclude from the control group.
 
     Returns:
         list: List of states selected as the control group.
@@ -126,6 +127,9 @@ def select_controls(
         f"select_controls called: treatment_group={treatment_group}, min_correlation={min_correlation}"
     )
 
+    if excluded_control_locations is None:
+        excluded_control_locations = []
+
     control_group = set()
 
     for treatment_location in treatment_group:
@@ -140,6 +144,7 @@ def select_controls(
             treatment_row[
                 (treatment_row >= min_correlation)
                 & (~treatment_row.index.isin(treatment_group))
+                & (~treatment_row.index.isin(excluded_control_locations))
             ]
             .sort_values(ascending=False)
             .index.tolist()
@@ -150,7 +155,10 @@ def select_controls(
                 f"No states meet min_correlation {min_correlation} for {treatment_location}, using fallback"
             )
             similar_states = (
-                treatment_row[~treatment_row.index.isin(treatment_group)]
+                treatment_row[
+                    ~treatment_row.index.isin(treatment_group)
+                    & (~treatment_row.index.isin(excluded_control_locations))
+                ]
                 .sort_values(ascending=False)
                 .head(fallback_n)
                 .index.tolist()
@@ -343,7 +351,7 @@ def smape(A, F):
 
 
 def evaluate_group(
-    treatment_group, data, total_Y, correlation_matrix, min_holdout, df_pivot, treatment_period=None
+    treatment_group, data, total_Y, correlation_matrix, min_holdout, df_pivot, treatment_period=None, excluded_control_locations=None
 ):
     """
     Evaluates a treatment group and returns error metrics.
@@ -377,6 +385,7 @@ def evaluate_group(
         correlation_matrix=correlation_matrix,
         treatment_group=treatment_group,
         min_correlation=0.8,
+        excluded_control_locations=excluded_control_locations,
     )
     logger.debug(f"Control group selected: {control_group}")
 
@@ -462,7 +471,7 @@ def evaluate_group(
 
 
 def select_treatments_exclusive(
-    similarity_matrix, treatment_size, excluded_locations, used_treatment_locations=None
+    similarity_matrix, treatment_size, excluded_treatment_locations, used_treatment_locations=None
 ):
     """
     Improved treatment selection for multi-cell mode ensuring treatment location exclusivity.
@@ -471,7 +480,7 @@ def select_treatments_exclusive(
     Args:
         similarity_matrix (pd.DataFrame): DataFrame containing correlations between locations
         treatment_size (int): Number of treatments to select for each combination
-        excluded_locations (list): List of locations to exclude globally
+        excluded_treatment_locations (list): List of locations to exclude globally
         used_treatment_locations (set): Set of treatment locations already used in previous cells
         
     Returns:
@@ -480,9 +489,9 @@ def select_treatments_exclusive(
     if used_treatment_locations is None:
         used_treatment_locations = set()
 
-    # Filter out empty strings from excluded_locations
-    excluded_locations = [loc for loc in excluded_locations if loc.strip()]
-    all_excluded = set(excluded_locations) | used_treatment_locations
+    # Filter out empty strings from excluded_treatment_locations
+    excluded_treatment_locations = [loc for loc in excluded_treatment_locations if loc.strip()]
+    all_excluded = set(excluded_treatment_locations) | used_treatment_locations
 
     logger.debug(
         f"select_treatments_exclusive: treatment_size={treatment_size}, excluded={len(all_excluded)} locations"
@@ -490,7 +499,7 @@ def select_treatments_exclusive(
 
     missing_locations = [
         location
-        for location in excluded_locations
+        for location in excluded_treatment_locations
         if location not in similarity_matrix.index
         or location not in similarity_matrix.columns
     ]
@@ -564,9 +573,10 @@ def select_controls_exclusive(
     correlation_matrix,
     treatment_group,
     used_treatment_locations=None,
-    excluded_locations=None,
+    excluded_treatment_locations=None,
     min_correlation=0.8,
     fallback_n=1,
+    excluded_control_locations=None,
 ):
     """
     Dynamically selects control group states based on correlation values.
@@ -574,31 +584,35 @@ def select_controls_exclusive(
     - Excludes current treatment group locations
     - Excludes globally excluded locations
     - Excludes locations that have been used as treatment in previous cells
+    - Excludes explicitly excluded control locations
     - ALLOWS reuse of control locations from previous cells
 
     Args:
         correlation_matrix (pd.DataFrame): Correlation matrix between states.
         treatment_group (list): List of states in the treatment group.
         used_treatment_locations (set): Set of treatment locations already used in previous cells.
-        excluded_locations (list): List of globally excluded locations.
+        excluded_treatment_locations (list): List of globally excluded locations.
         min_correlation (float): Minimum correlation threshold to consider a state as part of the control group.
         fallback_n (int): Number of top correlated states to select if no state meets the min_correlation.
+        excluded_control_locations (list): List of states to exclude from the control group.
 
     Returns:
         list: List of states selected as the control group.
     """
     if used_treatment_locations is None:
         used_treatment_locations = set()
-    if excluded_locations is None:
-        excluded_locations = []
+    if excluded_treatment_locations is None:
+        excluded_treatment_locations = []
+    if excluded_control_locations is None:
+        excluded_control_locations = []
 
     logger.debug(
-        f"select_controls_exclusive called: treatment_group={treatment_group}, used_treatment_locations={len(used_treatment_locations)}, excluded_locations={len(excluded_locations)}"
+        f"select_controls_exclusive called: treatment_group={treatment_group}, used_treatment_locations={len(used_treatment_locations)}, excluded_treatment_locations={len(excluded_treatment_locations)}"
     )
 
     control_group = set()
     all_excluded = (
-        set(treatment_group) | used_treatment_locations | set(excluded_locations)
+        set(treatment_group) | used_treatment_locations | set(excluded_treatment_locations) | set(excluded_control_locations)
     )
 
     for treatment_location in treatment_group:
@@ -655,8 +669,9 @@ def evaluate_group_exclusive(
     min_holdout,
     df_pivot,
     used_treatment_locations=None,
-    excluded_locations=None,
+    excluded_treatment_locations=None,
     treatment_period=None,
+    excluded_control_locations=None,
 ):
     """
     Evaluates a treatment group with location exclusivity for multi-cell mode.
@@ -672,7 +687,7 @@ def evaluate_group_exclusive(
         min_holdout (float): Minimum required holdout percentage
         df_pivot (pd.DataFrame): Pivoted data with time as index and locations as columns
         used_treatment_locations (set): Set of locations already used as treatment in other cells
-        excluded_locations (list): List of globally excluded locations
+        excluded_treatment_locations (list): List of globally excluded locations
         treatment_period (int): Number of periods for treatment (if None, uses 80/20 split)
     
     Returns:
@@ -702,8 +717,9 @@ def evaluate_group_exclusive(
         correlation_matrix=correlation_matrix,
         treatment_group=treatment_group,
         used_treatment_locations=used_treatment_locations,
-        excluded_locations=excluded_locations,
+        excluded_treatment_locations=excluded_treatment_locations,
         min_correlation=0.8,
+        excluded_control_locations=excluded_control_locations,
     )
     logger.debug(f"Control group selected: {control_group}")
 
@@ -790,7 +806,7 @@ def evaluate_group_exclusive(
 
 def BetterGroups(
     similarity_matrix,
-    excluded_locations,
+    excluded_treatment_locations,
     data,
     correlation_matrix,
     maximum_treatment_percentage=0.50,
@@ -798,6 +814,7 @@ def BetterGroups(
     status_updater=None,
     multicell_config=None,
     global_optimization=False,
+    excluded_control_locations=None,
 ):
     """
     Enhanced simulates and evaluates treatment groups for geo-experiments.
@@ -809,7 +826,7 @@ def BetterGroups(
     
     Args:
         similarity_matrix (pd.DataFrame): Correlation matrix for treatment selection
-        excluded_locations (list): List of locations to exclude from treatment selection
+        excluded_treatment_locations (list): List of locations to exclude from treatment selection
         data (pd.DataFrame): Input data with 'location', 'time', and 'Y' columns
         correlation_matrix (pd.DataFrame): Market correlation matrix for control selection
         maximum_treatment_percentage (float): Maximum treatment percentage (default: 0.50)
@@ -829,8 +846,8 @@ def BetterGroups(
     """
     unique_locations = data["location"].unique()
     no_locations = len(unique_locations)
-    # max_group_size = round(no_locations * 0.35)
-    # min_elements_in_treatment = round(no_locations * 0.20)
+    #max_group_size = round(no_locations * 0.35)
+    #min_elements_in_treatment = round(no_locations * 0.20)
     max_group_size = round(no_locations * 0.45)
     min_elements_in_treatment = round(no_locations * 0.15)
     min_holdout = 100 - (maximum_treatment_percentage * 100)
@@ -856,7 +873,7 @@ def BetterGroups(
                 similarity_matrix=similarity_matrix,
                 allowed_sizes=sizes,
                 total_cells_needed=top_n,
-                excluded_locations=excluded_locations,
+                excluded_treatment_locations=excluded_treatment_locations,
                 data=data,
                 correlation_matrix=correlation_matrix,
                 maximum_treatment_percentage=maximum_treatment_percentage,
@@ -872,7 +889,7 @@ def BetterGroups(
         for size in sizes:
 
             groups = select_treatments_exclusive(
-                similarity_matrix, size, excluded_locations, used_treatment_locations
+                similarity_matrix, size, excluded_treatment_locations, used_treatment_locations
             )
             if not groups:
                 logger.warning(
@@ -887,7 +904,7 @@ def BetterGroups(
                     select_treatments_exclusive(
                         similarity_matrix,
                         s,
-                        excluded_locations,
+                        excluded_treatment_locations,
                         used_treatment_locations,
                     )
                 )
@@ -908,7 +925,9 @@ def BetterGroups(
                     [min_holdout] * total_groups,
                     [df_pivot] * total_groups,
                     [used_treatment_locations] * total_groups,
-                    [excluded_locations] * total_groups,
+                    [excluded_treatment_locations] * total_groups,
+                    [None] * total_groups,
+                    [excluded_control_locations] * total_groups,
                 )
 
                 for idx, result in enumerate(futures):
@@ -976,7 +995,8 @@ def BetterGroups(
                     min_holdout=min_holdout,
                     df_pivot=df_pivot,
                     used_treatment_locations=current_used_treatments,
-                    excluded_locations=excluded_locations,
+                    excluded_treatment_locations=excluded_treatment_locations,
+                    excluded_control_locations=excluded_control_locations,
                 )
                 if result is not None:
                     final_results.append(result)
@@ -1019,7 +1039,7 @@ def BetterGroups(
     logger.info(f"Starting single-cell mode")
     possible_groups = []
     for size in range(min_elements_in_treatment, max_group_size + 1):
-        groups = select_treatments(similarity_matrix, size, excluded_locations)
+        groups = select_treatments(similarity_matrix, size, excluded_treatment_locations)
         possible_groups.extend(groups)
 
     if not possible_groups:
@@ -1038,6 +1058,8 @@ def BetterGroups(
             [correlation_matrix] * total_groups,
             [min_holdout] * total_groups,
             [df_pivot] * total_groups,
+            [None] * total_groups,
+            [excluded_control_locations] * total_groups,
         )
         for idx, result in enumerate(futures):
             results.append(result)
@@ -1153,7 +1175,7 @@ def update_progress(selected_count):
     return selected_cells
 
 
-def _validate_multicell_config(similarity_matrix, allowed_sizes, total_cells_needed, excluded_locations, data):
+def _validate_multicell_config(similarity_matrix, allowed_sizes, total_cells_needed, excluded_treatment_locations, data):
     """
     Validate multicell configuration and provide actionable error messages.
     
@@ -1161,7 +1183,7 @@ def _validate_multicell_config(similarity_matrix, allowed_sizes, total_cells_nee
         similarity_matrix: Correlation matrix for treatment selection
         allowed_sizes: List of allowed cell sizes to choose from
         total_cells_needed: Total number of cells in final experiment
-        excluded_locations: Globally excluded locations
+        excluded_treatment_locations: Globally excluded locations
         data: Input data
         
     Returns:
@@ -1173,7 +1195,7 @@ def _validate_multicell_config(similarity_matrix, allowed_sizes, total_cells_nee
     
     unique_locations = data["location"].unique()
     total_locations = len(unique_locations)
-    excluded_count = len(set(excluded_locations))
+    excluded_count = len(set(excluded_treatment_locations))
     available_locations = total_locations - excluded_count
     
     # Check basic feasibility
@@ -1228,7 +1250,7 @@ def optimize_global_multicell(
     similarity_matrix,
     allowed_sizes,
     total_cells_needed,
-    excluded_locations,
+    excluded_treatment_locations,
     data,
     correlation_matrix,
     maximum_treatment_percentage,
@@ -1246,7 +1268,7 @@ def optimize_global_multicell(
         similarity_matrix: Correlation matrix for treatment selection
         allowed_sizes: List of allowed cell sizes to choose from
         total_cells_needed: Total number of cells in final experiment
-        excluded_locations: Globally excluded locations
+        excluded_treatment_locations: Globally excluded locations
         data: Input data
         correlation_matrix: Market correlation matrix
         maximum_treatment_percentage: Max treatment percentage
@@ -1264,7 +1286,7 @@ def optimize_global_multicell(
     
     # Pre-flight validation
     is_valid, warnings, suggestions = _validate_multicell_config(
-        similarity_matrix, allowed_sizes, total_cells_needed, excluded_locations, data
+        similarity_matrix, allowed_sizes, total_cells_needed, excluded_treatment_locations, data
     )
     
     for warning in warnings:
@@ -1297,7 +1319,7 @@ def optimize_global_multicell(
         logger.info(f"Generating candidates for size {size}")
 
         groups = select_treatments_exclusive(
-            similarity_matrix, size, excluded_locations, used_treatment_locations=set()
+            similarity_matrix, size, excluded_treatment_locations, used_treatment_locations=set()
         )
 
         if not groups:
@@ -1315,7 +1337,9 @@ def optimize_global_multicell(
                 [min_holdout] * len(groups),
                 [df_pivot] * len(groups),
                 [set()] * len(groups),  # No used locations in phase 1
-                [excluded_locations] * len(groups),
+                [excluded_treatment_locations] * len(groups),
+                [None] * len(groups),
+                [excluded_control_locations] * len(groups),
             )
 
             for result in futures:
@@ -1391,7 +1415,7 @@ def optimize_global_multicell(
             correlation_matrix=correlation_matrix,
             treatment_group=treatment_group,
             used_treatment_locations=all_treatment_locations,  # Exclude ALL treatments
-            excluded_locations=excluded_locations,
+            excluded_treatment_locations=excluded_treatment_locations,
             min_correlation=0.8,
         )
         
@@ -2049,7 +2073,7 @@ def run_geo_analysis_streamlit_app(
     significance_level,
     deltas_range,
     periods_range,
-    excluded_locations,
+    excluded_treatment_locations,
     progress_bar_1=None,
     status_text_1=None,
     progress_bar_2=None,
@@ -2061,6 +2085,7 @@ def run_geo_analysis_streamlit_app(
     inference_type="iid",
     global_optimization=False,
     progress_updater=None,
+    excluded_control_locations=None,
 ):
     """
     Runs a complete geo analysis pipeline including market correlation, group optimization,
@@ -2072,7 +2097,7 @@ def run_geo_analysis_streamlit_app(
         significance_level (float): Significance level for statistical testing.
         deltas_range (tuple): Range of delta values to evaluate as (start, stop, step).
         periods_range (tuple): Range of treatment periods to evaluate as (start, stop, step).
-        excluded_locations (list): List of states to exclude from the analysis.
+        excluded_treatment_locations (list): List of states to exclude from the analysis.
         progress_bar_1 (callable): Progress bar updater for group optimization phase.
         status_text_1 (callable): Status text updater for group optimization phase.
         progress_bar_2 (callable): Progress bar updater for sensitivity evaluation phase.
@@ -2099,7 +2124,8 @@ def run_geo_analysis_streamlit_app(
     logger.info(f"  - significance_level: {significance_level}")
     logger.info(f"  - deltas_range: {deltas_range}")
     logger.info(f"  - periods_range: {periods_range}")
-    logger.info(f"  - excluded_locations: {excluded_locations}")
+    logger.info(f"  - excluded_treatment_locations: {excluded_treatment_locations}")
+    logger.info(f"  - excluded_control_locations: {excluded_control_locations}")
     logger.info(f"  - multicell_config: {multicell_config}")
     logger.info("=" * 80)
 
@@ -2125,13 +2151,14 @@ def run_geo_analysis_streamlit_app(
     simulation_results = BetterGroups(
         similarity_matrix=correlation_matrix,
         maximum_treatment_percentage=maximum_treatment_percentage,
-        excluded_locations=excluded_locations,
+        excluded_treatment_locations=excluded_treatment_locations,
         data=data,
         correlation_matrix=correlation_matrix,
         progress_updater=progress_bar_1,
         status_updater=status_text_1,
         multicell_config=multicell_config,
         global_optimization=global_optimization,
+        excluded_control_locations=excluded_control_locations,
     )
 
     if simulation_results is None:
@@ -2245,7 +2272,7 @@ def run_geo_analysis(
     significance_level,
     deltas_range,
     periods_range,
-    excluded_locations,
+    excluded_treatment_locations,
     progress_bar_1=None,
     status_text_1=None,
     progress_bar_2=None,
@@ -2266,7 +2293,7 @@ def run_geo_analysis(
         significance_level (float): Significance level for statistical testing.
         deltas_range (tuple): Range of delta values to evaluate as (start, stop, step).
         periods_range (tuple): Range of treatment periods to evaluate as (start, stop, step).
-        excluded_locations (list): List of states to exclude from the analysis.
+        excluded_treatment_locations (list): List of states to exclude from the analysis.
         progress_bar_1 (optional): First progress bar for UI updates.
         status_text_1 (optional): First status text for UI updates.
         progress_bar_2 (optional): Second progress bar for UI updates.
@@ -2290,7 +2317,8 @@ def run_geo_analysis(
     logger.info(f"  - significance_level: {significance_level}")
     logger.info(f"  - deltas_range: {deltas_range}")
     logger.info(f"  - periods_range: {periods_range}")
-    logger.info(f"  - excluded_locations: {excluded_locations}")
+    logger.info(f"  - excluded_treatment_locations: {excluded_treatment_locations}")
+    logger.info(f"  - excluded_control_locations: {excluded_control_locations}")
     logger.info("=" * 80)
 
     if progress_bar_1 or progress_bar_2 or status_text_1 or status_text_2 is None:
@@ -2306,11 +2334,12 @@ def run_geo_analysis(
     simulation_results = BetterGroups(
         similarity_matrix=correlation_matrix,
         maximum_treatment_percentage=maximum_treatment_percentage,
-        excluded_locations=excluded_locations,
+        excluded_treatment_locations=excluded_treatment_locations,
         data=data,
         correlation_matrix=correlation_matrix,
         progress_updater=progress_bar_1,
         status_updater=status_text_1,
+        excluded_control_locations=excluded_control_locations,
     )
 
     # Step 3: Evaluate sensitivity for different deltas and periods
diff --git a/Murray/post_analysis.py b/Murray/post_analysis.py
index 47d32a1..022bff0 100644
--- a/Murray/post_analysis.py
+++ b/Murray/post_analysis.py
@@ -18,6 +18,7 @@ def run_geo_evaluation(
     n_permutations=50000,
     inference_type="iid",
     significance_level=0.1,
+    excluded_control_locations=None,
 ):
     logger.info("Starting run_geo_evaluation")
     logger.info(f"Input data shape: {data_input.shape}")
@@ -55,6 +56,7 @@ def smape(A, F):
         correlation_matrix=correlation_matrix,
         treatment_group=treatment_group,
         min_correlation=0.8,
+        excluded_control_locations=excluded_control_locations,
     )
     logger.info(f"Control group selected: {control_group}")
 
@@ -100,6 +102,13 @@ def smape(A, F):
     time_train = time_index[:start_position_treatment]
     time_test = time_index[start_position_treatment:]
 
+    if len(X_train) == 0:
+        raise ValueError(
+            f"No pre-treatment periods available for training. "
+            f"The treatment start date ({start_treatment}) is at or before the beginning of the dataset. "
+            f"Please select a later treatment start date."
+        )
+
     logger.info("Fitting synthetic control model...")
     model = SyntheticControl(use_ridge_adjustment=True, ridge_alpha=1.0)
     model.fit(X_train, y_train, time_train=time_train)
diff --git a/api.py b/api.py
index 2d781be..43182f1 100644
--- a/api.py
+++ b/api.py
@@ -124,7 +124,8 @@ async def analyze_design(
     date_column: str = Form(None),
     location_column: str = Form(None),
     target_column: str = Form(None),
-    excluded_locations: str = Form(None),
+    excluded_treatment_locations: str = Form(None),
+    excluded_control_locations: str = Form(None),
     maximum_treatment_percentage: float = Form(0.3),
     significance_level: float = Form(0.1),
     deltas_range: str = Form("0.01,0.1,0.01"),
@@ -179,12 +180,17 @@ async def analyze_design(
         # Parse parameters
         deltas_range = tuple(map(float, deltas_range.split(',')))
         periods_range = tuple(map(int, periods_range.split(',')))
-        # Handle empty excluded_locations properly
-        if excluded_locations and excluded_locations.strip():
-            excluded_locations = tuple(map(str, excluded_locations.split(',')))
+        # Handle empty excluded_treatment_locations properly
+        if excluded_treatment_locations and excluded_treatment_locations.strip():
+            excluded_treatment_locations = tuple(map(str, excluded_treatment_locations.split(',')))
         else:
-            excluded_locations = tuple()
-        
+            excluded_treatment_locations = tuple()
+        # Handle empty excluded_control_locations properly
+        if excluded_control_locations and excluded_control_locations.strip():
+            excluded_control_locations = tuple(map(str, excluded_control_locations.split(',')))
+        else:
+            excluded_control_locations = tuple()
+
         # Process multicell parameters
         multicell_config = None
         if enable_multicell:
@@ -220,7 +226,8 @@ async def analyze_design(
             date_column=date_column,
             location_column=location_column,
             target_column=target_column,
-            excluded_locations=excluded_locations,
+            excluded_treatment_locations=excluded_treatment_locations,
+            excluded_control_locations=excluded_control_locations,
             maximum_treatment_percentage=maximum_treatment_percentage,
             significance_level=significance_level,
             deltas_range=deltas_range,
diff --git a/experimental_design.py b/experimental_design.py
index 54dc01d..d10b963 100644
--- a/experimental_design.py
+++ b/experimental_design.py
@@ -764,9 +764,12 @@ def reset_states():
                 unsafe_allow_html=True,
             )
             # Location exclusion and test type selection
-            excluded_locations = st.multiselect(
+            excluded_treatment_locations = st.multiselect(
                 "Select excluded locations", cleaned["location"].unique()
             )
+            excluded_control_locations = st.multiselect(
+                "Select excluded controls", cleaned["location"].unique()
+            )
 
             # Analyze data and recommend statistical test
             data_analysis = analyze_data_characteristics(cleaned, col_target="Y")
@@ -954,7 +957,8 @@ def reset_states():
 
             # Track parameter changes to reset simulation state
             current_params = {
-                "excluded_locations": excluded_locations,
+                "excluded_treatment_locations": excluded_treatment_locations,
+                "excluded_control_locations": excluded_control_locations,
                 "maximum_treatment_percentage_pre": maximum_treatment_percentage_pre,
                 "significance_level_pre": significance_level_pre,
                 "deltas_range": (delta_min, delta_max, delta_step),
@@ -1016,7 +1020,8 @@ def reset_states():
                         # Run main geo analysis simulation
                         results = run_geo_analysis_streamlit_app(
                             data=cleaned,
-                            excluded_locations=excluded_locations,
+                            excluded_treatment_locations=excluded_treatment_locations,
+                            excluded_control_locations=excluded_control_locations,
                             maximum_treatment_percentage=maximum_treatment_percentage,
                             significance_level=significance_level,
                             deltas_range=deltas_range,
@@ -1055,7 +1060,8 @@ def reset_states():
                         # Show current settings for debugging
                         with st.expander("🔧 Current Analysis Settings", expanded=False):
                             st.write(f"**Maximum Treatment Percentage:** {maximum_treatment_percentage*100:.1f}%")
-                            st.write(f"**Excluded Locations:** {len(excluded_locations)} locations")
+                            st.write(f"**Excluded Locations:** {len(excluded_treatment_locations)} locations")
+                            st.write(f"**Excluded Controls:** {len(excluded_control_locations)} controls")
                             st.write(f"**Total Locations Available:** {len(cleaned['location'].unique())} locations")
                             st.write(f"**Time Periods:** {len(cleaned['time'].unique())} periods")
                             st.write(f"**Total Y Sum:** {cleaned['Y'].sum():,.2f}")
diff --git a/experimental_evaluation.py b/experimental_evaluation.py
index 5cb5b4f..f24d7b7 100644
--- a/experimental_evaluation.py
+++ b/experimental_evaluation.py
@@ -766,6 +766,9 @@ def reset_states():
             treatment_group = st.multiselect(
                 "Select treatment group", data1["location"].unique()
             )
+            excluded_control_locations = st.multiselect(
+                "Select excluded control locations", data1["location"].unique()
+            )
             spend = st.number_input("Select spend")
             mmm_option = st.selectbox(
                 "Select the option to calculate the iROAS or iCPA", ["iROAS", "iCPA"]
@@ -795,6 +798,7 @@ def reset_states():
                 "start_treatment": start_treatment,
                 "end_treatment": end_treatment,
                 "treatment_group": treatment_group,
+                "excluded_control_locations": excluded_control_locations,
                 "spend": spend,
                 "mmm_option": mmm_option,
                 "col_target": col_target,
@@ -814,14 +818,20 @@ def reset_states():
                     update_metrics("experimental_evaluation")
 
                     with st.spinner("Running analysis..."):
+                        try:
+                            results = run_geo_evaluation(
+                                data1,
+                                start_treatment,
+                                end_treatment,
+                                treatment_group,
+                                spend,
+                                excluded_control_locations=excluded_control_locations,
+                            )
+                        except ValueError as e:
+                            st.error(f"Error: {e}")
+                            st.session_state.evaluation_button_clicked = False
+                            st.stop()
 
-                        results = run_geo_evaluation(
-                            data1,
-                            start_treatment,
-                            end_treatment,
-                            treatment_group,
-                            spend,
-                        )
                         treatment = results["treatment"]
                         st.session_state.treatment = treatment
                         counterfactual = results["counterfactual"]
diff --git a/tasks.py b/tasks.py
index f6562be..10d0e83 100644
--- a/tasks.py
+++ b/tasks.py
@@ -106,7 +106,8 @@ def analyze_design_task(
     date_column: str,
     location_column: str,
     target_column: str,
-    excluded_locations: tuple,
+    excluded_treatment_locations: tuple,
+    excluded_control_locations: tuple,
     maximum_treatment_percentage: float,
     significance_level: float,
     deltas_range: tuple,
@@ -257,7 +258,8 @@ def advance_to_sensitivity():
         
         results = run_geo_analysis_streamlit_app(
             data=data,
-            excluded_locations=excluded_locations,
+            excluded_treatment_locations=excluded_treatment_locations,
+            excluded_control_locations=excluded_control_locations,
             maximum_treatment_percentage=maximum_treatment_percentage,
             significance_level=significance_level,
             deltas_range=deltas_range,