An AI-powered Bayesian generative modeling approach for causal inference in observational studies

Music City Center 

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 variables. The core innovation of CausalBGM lies in its ability to estimate the individual treatment effect (ITE) by learning individual-specific distributions of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and outcome. This approach not only effectively mitigates confounding effects but also provides comprehensive uncertainty quantification, offering reliable and interpretable causal effect estimates at the individual level. This framework leverages the power of AI to capture complex dependencies among variables while adhering to the Bayesian principles. Its Bayesian foundation ensures statistical rigor, providing robust and well-calibrated posterior intervals. By addressing key limitations of existing methods, CausalBGM emerges as a robust and promising framework for advancing causal inference in modern applications.

Treatment Effect

Bayesian Deep Learning

Markov chain Monte Carlo

Dose-response Function

Potential Outcome

Uncertainty Quantification 

Section on Statistical Learning and Data Science