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| # 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 |