4 papers
BGM-IV: an AI-powered Bayesian generative modeling approach for instrumental variable analysis
Guyue Luo, Qiao Liu
Instrumental-variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects and high-dimensional covariates…
Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
Qiao Liu
Missing data imputation remains a fundamental challenge in modern data science, especially when uncertainty quantification is essential. In this work, we propose MissBGM, an AI-pow…
An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
Qiao Liu, Wing Hung Wong
Modern data analysis increasingly requires flexible conditional inference P(X_B | X_A) where (X_A, X_B) is an arbitrary partition of observed variable X. Existing approaches are ei…
An AI-powered Bayesian generative modeling approach for causal inference in observational studies
Qiao Liu, Wing Hung Wong
Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling appro…