1 citations · 1 across the 4 of their papers we have counts for
5 papers
BGM-IV: AI-Powered Bayesian Generative Modeling for Instrumental Variable Regression with High-Dimensional Covariates
Guyue Luo, Qiao Liu, Haidong Lu +1
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…
CausalEGM: a general causal inference framework by encoding generative modeling
Qiao Liu, Zhongren Chen, Wing Hung Wong
Although understanding and characterizing causal effects have become essential in observational studies, it is challenging when the confounders are high-dimensional. In this articl…