Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias

The paper introduces a new algorithm that uses decision trees and random forests to estimate individual treatment effects while providing interpretability. It modifies the standard random forest splitting criterion by combining a heterogeneity-focused criterion with a bias-correction criterion, enabling the model to handle observational studies with varying treatment propensities without separately estimating propensity scores. The resulting tree structure directly reveals which features drive treatment effect differences, and simulation studies show the method matches or surpasses existing approaches in prediction accuracy while improving interpretability.