【摘要】 目的 探讨观察性研究中贝叶斯加性回归树估计平均处理效应的统计学性能及适用条件。方法 通过模拟试验和实例分析比较贝叶斯加性回归树与多变量回归、倾向性得分匹配、逆概率加权的估计结果差异。结果 模拟试验表明，在线性假设下，贝叶斯加性回归树的估计表现与常用方法接近。当数据中变量之间关系复杂，存在非线性关系时，贝叶斯加性回归树的估计结果明显优于其他方法。当不满足可忽略性假设时，未观测到的混杂因素导致四种估计方法的结果都会存在偏倚，但是贝叶斯加性回归树的估计偏倚明显小于其他三种方法，结果相对稳健。并在实例中使用该方法估计了戒烟对于体重变化的平均处理效应。结论 在绝大多数观察性研究中，研究结局受到多个因素的影响，研究者很难正确指定变量间的关系。从模型拟合和结果准确性来说，贝叶斯加性回归树是值得推荐的方法。更多还原

【Abstract】 Objective To explore the statistical performance and applicable conditions of Bayesian additive regression tree(BART) for estimating average treatment effect in observational studies. Methods The difference of estimates between BART and multivariate regression, propensity score matching, and inverse probability weighting through simulations and actual epidemiological data was compared. Results The results of these simulations showed that under the linear assumption, the performance of BART was close to that of the commonly used methods; when the relationship among variables in the data was complex and non-linear, BART performed markedly better than the others. When the ignorability assumption was not satisfied and there was unobserved confounding, all methods performed worse, but BART was still significantly better than the others and relatively robust. In the actual epidemiological data, this method was used to estimate the average treatment effect of smoking cessation on weight change. Conclusion In most observational studies, outcomes are influenced by multiple factors, making it difficult for researchers to properly specify relationships between variables. It is difficult to identify all these variables or determine the relationship between them. In terms of model fitting and result accuracy, BART is worth recommending.更多还原