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.

翻译：确定对具体干预(医疗或政策)反应特别好(或差)的分组,需要有专门为因果推断而专门设计的新的受监督的学习方法。Bayesian Causal Forest(BCF)是记录下来的一种最新方法,用于很好地进行数据生成过程,在许多应用中,这种过程在很大程度上混淆了合理性。本文为适应BCFF模型开发了一种新的算法,比以前Gibbbs取样员更有效率。新的算法可用于初始化现有的Gibbs取样员的独立链,从而在模拟研究中更好地进行后方探索并覆盖相关间隔估计数。新的算法通过模拟研究以及经验分析,与相关方法进行比较。