A General Bayesian Nonparametric Approach for Estimating Population-Level and Conditional Causal Effects
总体与条件因果效应估计的一般贝叶斯非参数方法
We propose a Bayesian nonparametric (BNP) approach to causal inference using observational data consisting of outcome, treatment, and a set of confounders. The conditional distribution of the outcome given treatment and confounders is modeled flexibly using a dependent nonparametric mixture model, in which both the atoms and the weights vary with the confounders. The proposed BNP model is well suited for causal inference problems, as it does not rely on parametric…