An AI-powered Bayesian Generative Modeling Approach for Causal Inference in Observational Studies
观察性研究中用于因果推断的人工智能贝叶斯生成模型
Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in…