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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 |