论文标题
随机树合奏用于估计异质效应
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
论文作者
论文摘要
确定对特定干预措施（医疗或政策）响应特别好（或较差）的亚组需要专门针对因果推断量身定制的新监督学习方法。贝叶斯因果森林（BCF）是一种最近的方法，已记录在数据生成过程中具有很好的混杂性，这些方法在许多应用中具有合理的混杂性。本文开发了一种用于拟合BCF模型的新型算法，该算法比先前可用的Gibbs采样器更有效。新算法可用于初始化现有Gibbs采样器的独立链，从而使模拟研究中相关间隔估计值的后验探索和覆盖率更好。通过模拟研究和经验分析将新算法与相关方法进行比较。
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.
