Stochastic Tree Ensembles for Estimating Heterogeneous Effects
This work provides a more efficient method for researchers and practitioners using causal inference to identify subgroups with differential treatment responses, though it is incremental as it builds directly on existing BCF methodology.
The paper tackles the problem of efficiently fitting Bayesian Causal Forest models for estimating heterogeneous treatment effects, developing a novel algorithm that improves computational efficiency and enhances posterior exploration in simulations.
Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for causal inference. Bayesian Causal Forest (BCF) is a recent method that has been documented to perform well on data generating processes with strong confounding of the sort that is plausible in many applications. This paper develops a novel algorithm for fitting the BCF model, which is more efficient than the previously available Gibbs sampler. The new algorithm can be used to initialize independent chains of the existing Gibbs sampler leading to better posterior exploration and coverage of the associated interval estimates in simulation studies. The new algorithm is compared to related approaches via simulation studies as well as an empirical analysis.