There is a dearth of robust methods to estimate the causal effects of
multiple treatments when the outcome is binary. This paper uses two unique sets
of simulations to propose and evaluate the use of Bayesian Additive Regression
Trees (BART) in such settings. First, we compare BART to several approaches
that have been proposed for continuous outcomes, including inverse probability
of treatment weighting (IPTW), targeted maximum likelihood estimator (TMLE),
vector matching and regression adjustment. Results suggest that under
conditions of non-linearity and non-additivity of both the treatment assignment
and outcome generating mechanisms, BART, TMLE and IPTW using generalized
boosted models (GBM) provide better bias reduction and smaller root mean
squared error. BART and TMLE provide more consistent 95 per cent CI coverage
and better large-sample convergence property. Second, we supply BART with a
strategy to identify a common support region for retaining inferential units
and for avoiding extrapolating over areas of the covariate space where common
support does not exist. BART retains more inferential units than the
generalized propensity score based strategy, and shows lower bias, compared to
TMLE or GBM, in a variety of scenarios differing by the degree of covariate
overlap. A case study examining the effects of three surgical approaches for
non-small cell lung cancer demonstrates the methods.

本文提出并评估贝叶斯加性回归树（BART）与其他已有方法（如 IPTWM、TMLE、向量匹配和回归调整等）在多个处理方式时，因为结果是二进制的而缺乏强大的估计因果效应的方法。结果表明，在处理分配和结果生成机制的非线性和非可加性的情况下，BART、TMLE 和采用广义提升模型（GBM）的 IPTW 提供更好的偏差降低和较小的均方根误差，本文还提出了一种用于特定处理的共同支持区域来获得更好的外推结果和保持推断的方法。