Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects
Journal:
arXiv
Published Date:
Apr 18, 2025
Abstract
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.
