Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects
This addresses the need for precise, interpretable treatment effect estimation in high-stakes applications like healthcare, though it appears incremental as an enhancement to existing tree-based methods.
The paper tackles the problem of high estimation errors in tree-based causal inference for heterogeneous treatment effects by proposing Dynamic Regularized Causal Boosted Decision Trees, which integrates variance regularization and calibration into gradient boosted trees. The method significantly improves estimation accuracy on benchmark and clinical data, achieving high accuracy in a patient triage study.
Heterogeneous treatment effect estimation in high-stakes applications demands models that simultaneously optimize precision, interpretability, and calibration. Many existing tree-based causal inference techniques, however, exhibit high estimation errors when applied to observational data because they struggle to capture complex interactions among factors and rely on static regularization schemes. In this work, we propose Dynamic Regularized Causal Boosted Decision Trees (CBDT), a novel framework that integrates variance regularization and average treatment effect calibration into the loss function of gradient boosted decision trees. Our approach dynamically updates the regularization parameters using gradient statistics to better balance the bias-variance tradeoff. Extensive experiments on standard benchmark datasets and real-world clinical data demonstrate that the proposed method significantly improves estimation accuracy while maintaining reliable coverage of true treatment effects. In an intensive care unit patient triage study, the method successfully identified clinically actionable rules and achieved high accuracy in treatment effect estimation. The results validate that dynamic regularization can effectively tighten error bounds and enhance both predictive performance and model interpretability.