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. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and outcome. This individualized posterior representation yields estimates of the individual treatment effect (ITE) together with well-calibrated posterior intervals while mitigating confounding effect. CausalBGM is fitted through an iterative algorithm to update the model parameters and the latent features until convergence. This framework leverages the power of AI to capture complex dependencies among variables while adhering to the Bayesian principles. Extensive experiments demonstrate that CausalBGM consistently outperforms state-of-the-art methods, particularly in scenarios with high-dimensional covariates and large-scale datasets. By addressing key limitations of existing methods, CausalBGM emerges as a robust and promising framework for advancing causal inference in a wide range of modern applications. The code for CausalBGM is available at https://github.com/liuq-lab/bayesgm. The tutorial for CausalBGM is available at https://causalbgm.readthedocs.io.

翻译：高维协变量的观察性研究中的因果推断面临重大挑战。本文提出CausalBGM，一种基于人工智能的贝叶斯生成建模方法，能够捕捉协变量、处理变量与结果之间的因果关系。其核心创新在于通过学习驱动处理变量和结果变化的低维潜在特征集（如潜在混杂因子）的个体特异性分布，来估计个体处理效应（ITE）。这种个体化的后验表征在减轻混杂效应的同时，能够提供个体处理效应（ITE）的估计以及校准良好的后验区间。CausalBGM通过迭代算法进行拟合，持续更新模型参数和潜在特征直至收敛。该框架在遵循贝叶斯原理的同时，利用人工智能的强大能力捕捉变量间的复杂依赖关系。大量实验表明，CausalBGM在多种场景下始终优于现有最先进方法，特别是在高维协变量和大规模数据集的场景中。通过解决现有方法的关键局限性，CausalBGM成为一个稳健且前景广阔的框架，可广泛应用于推动现代应用中的因果推断研究。CausalBGM的代码发布于https://github.com/liuq-lab/bayesgm，相关教程可在https://causalbgm.readthedocs.io获取。