Bayesian Inference for Confounding Variables and Limited Information

Abstract

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 explicitly allow for latent confounding variables and partial information. Using the case of Simpson's paradox, we demonstrate how this approach produces logically consistent posterior distributions that widen credibly intervals in the presence of potential confounding. Our method provides a generalizable, information-theoretic foundation for more robust predictive inference in observational sciences.