--- a
+++ b/01_run_simulation_experiment.py
@@ -0,0 +1,108 @@
+import argparse
+import sys
+import os
+import wandb
+from typing import Any
+import pandas as pd 
+import src.iterpretability.logger as log
+# from src.iterpretability.experiments.experiments_ext import (
+#     PredictiveSensitivity,
+#     PropensitySensitivity,
+#     NonLinearitySensitivity,
+#     CohortSizeSensitivity,
+# )
+from src.iterpretability.simulators import TYSimulator, TSimulator
+
+from src.iterpretability.experiments.sample_size_sensitivity import SampleSizeSensitivity
+from src.iterpretability.experiments.important_feature_num_sensitivity import ImportantFeatureNumSensitivity
+from src.iterpretability.experiments.propensity_scale_sensitivity import PropensityScaleSensitivity
+from src.iterpretability.experiments.predictive_scale_sensitivity import PredictiveScaleSensitivity
+from src.iterpretability.experiments.treatment_space_sensitivity import TreatmentSpaceSensitivity
+from src.iterpretability.experiments.sample_size_sensitivity_visualization import SampleSizeSensitivityVisualization
+from src.iterpretability.experiments.data_dimensionality_sensitivity import DataDimensionalitySensitivity
+from src.iterpretability.experiments.feature_overlap_sensitivity import FeatureOverlapSensitivity
+from src.iterpretability.experiments.expertise_sensitivity import ExpertiseSensitivity
+
+
+from src.iterpretability.datasets.data_loader import load
+
+# Hydra for configuration
+import hydra
+from omegaconf import DictConfig, OmegaConf
+
+@hydra.main(config_path="conf", config_name="config_TY_tcga", version_base=None)
+def main(cfg: DictConfig):
+    ############################
+    # 1. SETUP
+    ############################
+    # print(f"Working directory : {os.getcwd()}")
+    # print(f"Output directory  : {hydra.core.hydra_config.HydraConfig.get().runtime.output_dir}")
+    
+    # Set logging level
+    log.add(sink=sys.stderr, level=cfg.log_level)
+
+    ############################
+    # 2. EXPERIMENTS
+    ############################
+    
+    if cfg.experiment_name == "sample_size_sensitivity":
+        exp = SampleSizeSensitivity(cfg)
+        exp.run()
+
+    elif cfg.experiment_name == "important_feature_num_sensitivity":
+        exp = ImportantFeatureNumSensitivity(cfg)
+        exp.run()
+
+    elif cfg.experiment_name == "propensity_scale_sensitivity":
+        exp = PropensityScaleSensitivity(cfg)
+        exp.run()
+
+    elif cfg.experiment_name == "predictive_scale_sensitivity":
+        if cfg.simulator.simulation_type == "T":
+            print("PredictiveScaleSensitivity is not supported for TSimulator")
+            return None
+        
+        exp = PredictiveScaleSensitivity(cfg)
+        exp.run()
+
+    elif cfg.experiment_name == "treatment_space_sensitivity":
+        if cfg.simulator.simulation_type == "T":
+            print("PredictiveScaleSensitivity is not supported for TSimulator")
+            return None
+        
+        exp = TreatmentSpaceSensitivity(cfg)
+        exp.run()
+
+    elif cfg.experiment_name == "sample_size_sensitivity_visualization":
+        if cfg.simulator.dim_Y != 1 or cfg.simulator.num_T != 2:
+            print("SampleSizeSensitivityVisualization is only supported for dim_Y=1 and dim_T=2")
+            return None
+        
+        if cfg.simulator.simulation_type == "T":
+            print("Make sure to only pick two treatments for this to work. And remove this if statement.")
+            return None 
+        
+        exp = SampleSizeSensitivityVisualization(cfg)
+        exp.run()
+
+    elif cfg.experiment_name == "data_dimensionality_sensitivity":
+        exp = DataDimensionalitySensitivity(cfg)
+        exp.run()
+
+    elif cfg.experiment_name == "feature_overlap_sensitivity":
+        if cfg.simulator.simulation_type == "T":
+            print("FeatureOverlapSensitivity is not supported for TSimulator")
+            return None
+        
+        exp = FeatureOverlapSensitivity(cfg)
+        exp.run()
+
+    elif cfg.experiment_name == "expertise_sensitivity":
+        exp = ExpertiseSensitivity(cfg)
+        exp.run()
+            
+    else:
+        raise ValueError(f"Invalid experiment name: {cfg.experiment_name}")
+    
+if __name__ == "__main__":
+    main()
\ No newline at end of file