# utils/treatment_effects.py
from sklearn.linear_model import LinearRegression, LogisticRegression
import pandas as pd
import numpy as np
# For matching-based methods, you might need libraries like dowhy or causalml
# import statsmodels.api as sm # Example for regression diagnostics

class TreatmentEffectAlgorithms:
    def linear_regression_ate(self, df, treatment_col, outcome_col, covariates):
        """
        Estimate ATE using linear regression.
        """
        X = df[covariates + [treatment_col]]
        y = df[outcome_col]
        model = LinearRegression()
        model.fit(X, y)
        ate = model.coef_[-1] # Coefficient of treatment_col
        return float(ate)

    def propensity_score_matching(self, df, treatment_col, outcome_col, covariates):
        """
        Placeholder for Propensity Score Matching.
        You would implement or integrate a matching algorithm here.
        """
        print("Propensity Score Matching is a placeholder. Returning a dummy ATE.")
        # Simplified: Estimate propensity scores
        X_propensity = df[covariates]
        T_propensity = df[treatment_col]
        prop_model = LogisticRegression(solver='liblinear')
        prop_model.fit(X_propensity, T_propensity)
        propensity_scores = prop_model.predict_proba(X_propensity)[:, 1]
        
        # Dummy ATE calculation for demonstration
        treated_outcome = df[df[treatment_col] == 1][outcome_col].mean()
        control_outcome = df[df[treatment_col] == 0][outcome_col].mean()
        return float(treated_outcome - control_outcome) # Simplified dummy ATE

    def inverse_propensity_weighting(self, df, treatment_col, outcome_col, covariates):
        """
        Placeholder for Inverse Propensity Weighting (IPW).
        You would implement or integrate IPW here.
        """
        print("Inverse Propensity Weighting is a placeholder. Returning a dummy ATE.")
        # Dummy ATE for demonstration
        return np.random.rand() * 10 # Random dummy value

    def t_learner(self, df, treatment_col, outcome_col, covariates):
        """
        Placeholder for T-learner.
        You would implement a T-learner using two separate models.
        """
        print("T-learner is a placeholder. Returning a dummy ATE.")
        # Dummy ATE for demonstration
        return np.random.rand() * 10 + 5 # Random dummy value

    def s_learner(self, df, treatment_col, outcome_col, covariates):
        """
        Placeholder for S-learner.
        You would implement an S-learner using a single model.
        """
        print("S-learner is a placeholder. Returning a dummy ATE.")
        # Dummy ATE for demonstration
        return np.random.rand() * 10 - 2 # Random dummy value