Causal Inference

We develop causal inference methodologies for high-dimensional biomedical data. By combining modern AI techniques and rigorious statistical inference (e.g., Bayesian inference), we aim to 1) estimate causal effects, especially under high-dimensional covaraites or even hidden confounding variable, 2) identify disease-relevant drivers (gene, regualtory elements, regulatory programs) and support interpretable biological discovery.

Related Papers

Guyue Luo, Qiao Liu (2026). BGM-IV: an AI-powered Bayesian generative modeling approach for instrumental variable analysis. arXiv preprint arXiv:2605.07029.

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Qiao Liu, Wing Hung Wong (2026). A Bayesian Generative Modeling Approach for Arbitrary Conditional Inference. arXiv, 2026.

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Qiao Liu, Wing Hung Wong (2025). An AI-powered Bayesian generative modeling approach for causal inference in observational studies. Journal of the American Statistical Association, 2026.

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Qiao Liu, Wanwen Zeng, Hongtu Zhu, Lexin Li, Wing Hung Wong (2024). Leveraging genomic large language models to enhance causal genotype-brain-clinical pathways in Alzheimer's disease. medRxiv, 2024.

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Qiao Liu, Zhongren Chen, Wing Hung Wong (2024). An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies. Proceedings of the National Academy of Sciences, 121(23), e2322376121, 2024.

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