Causal Inference Advanced

Advanced Causal Inference

Implementing advanced causal inference in ML — from DAG discovery and instrumental variables through double/debiased ML, heterogeneous treatment effects, and causal structure learning.

When to Use

• Estimating causal effects from observational data
• Discovering causal structure from data
• Learning heterogeneous treatment effects (CATE)
• Instrumental variable methods for unobserved confounding
• Double ML for high-dimensional causal inference

Advanced Methods

class DoubleML:
    """Double/Debiased Machine Learning for ATE estimation."""
    def __init__(self, model_y, model_t):
        self.model_y = model_y  # Outcome model
        self.model_t = model_t  # Treatment model
    
    def fit(self, X, T, Y):
        # Cross-fitting
        from sklearn.model_selection import KFold
        cv = KFold(n_splits=5)
        residuals_t = np.zeros_like(T, dtype=float)
        residuals_y = np.zeros_like(Y, dtype=float)
        
        for train_idx, test_idx in cv.split(X):
            self.model_t.fit(X[train_idx], T[train_idx])
            self.model_y.fit(X[train_idx], Y[train_idx])
            residuals_t[test_idx] = T[test_idx] - self.model_t.predict(X[test_idx])
            residuals_y[test_idx] = Y[test_idx] - self.model_y.predict(X[test_idx])
        
        # ATE = Cov(residuals_t, residuals_y) / Var(residuals_t)
        ate = np.cov(residuals_t, residuals_y)[0, 1] / np.var(residuals_t)
        return {'ate': round(ate, 4)}

Verification Checklist

• Causal DAG specified or discovered from data
• Identification strategy chosen (backdoor, IV, difference-in-differences, RDD)
• Double ML with cross-fitting for high-dimensional settings
• Heterogeneous treatment effects (CATE) estimated
• Sensitivity analysis for unmeasured confounding
• Overlap and positivity assumptions checked
• Results reported with confidence intervals