Bayesian Inference for Confounding Variables and Limited Information
混杂变量与有限信息下的贝叶斯推断
A central challenge in statistical inference is the presence of confounding variables that may distort observed associations between treatment and outcome. Conventional "causal" methods, grounded in assumptions such as ignorability, exclude the possibility of unobserved confounders, leading to posterior inferences that overstate certainty. We develop a Bayesian framework that relaxes these assumptions by introducing entropy-favoring priors over hypothesis spaces that…