Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation
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用于异质性处理效应估计的收缩贝叶斯因果森林
- 研究问题
- 在协变量很多且相关性稀疏的观察性数据中，如何估计异质性处理效应，并识别驱动效应差异的调节变量？
- 方法与思路
- 作者在贝叶斯因果森林中加入一对先验，通过树集成中各协变量的分裂次数调整权重，并允许纳入混杂变量的先验知识。
- 材料陈述的贡献
- 摘要称，新模型增强对稀疏生成机制的适应并实现贝叶斯特征收缩，通过模拟考察高维和强混杂场景，另给出实际应用。
- 阅读关注点
- 应核对两组先验分别作用于哪些函数、混杂与效应修饰变量如何区分，以及高维比较、先验敏感性和不确定性评估。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未说明完整因果识别条件，也未给出比较优势的具体数值。
原文摘要
This paper develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data. The sparsity-inducing component we introduce is motivated by empirical studies where not all the available covariates are relevant, leading to different degrees of sparsity underlying the surfaces of interest in the estimation of individual treatment effects. The extended version presented in this work, which we name Shrinkage Bayesian Causal Forest, is equipped with an additional pair of priors allowing the model to adjust the weight of each covariate through the corresponding number of splits in the tree ensemble. These priors improve the model's adaptability to sparse data generating processes and allow to perform fully Bayesian feature shrinkage in a framework for treatment effects estimation, and thus to uncover the moderating factors driving heterogeneity. In addition, the method allows prior knowledge about the relevant confounding covariates and the relative magnitude of their impact on the outcome to be incorporated in the model. We illustrate the performance of our method in simulated studies, in comparison to Bayesian Causal Forest and other state-of-the-art models, to demonstrate how it scales up with an increasing number of covariates and how it handles strongly confounded scenarios. Finally, we also provide an example of application using real-world data.