An AI-powered Bayesian generative modeling approach for causal inference in observational studies (2501.00755v1)

Abstract: 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. CausalBGM adopts a Bayesian model and uses a novel iterative algorithm to update the model parameters and the posterior distribution of 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. 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 in fields such as genomics, healthcare, and social sciences. CausalBGM is maintained at the website https://causalbgm.readthedocs.io/.

• The paper introduces CausalBGM, a robust AI-powered Bayesian generative model that mitigates confounding in high-dimensional observational data.
• Its novel iterative algorithm leverages mini-batch likelihood and parallel computation to efficiently estimate individual treatment effects.
• Empirical results show superior predictive accuracy and reliable uncertainty quantification compared to state-of-the-art causal inference methods.

CausalBGM: A Bayesian Generative Modeling Framework for Causal Inference in Observational Studies

The paper presents CausalBGM, an AI-powered Bayesian generative modeling approach designed to address challenges in causal inference within observational studies that involve high-dimensional covariates. Traditional methods for causal inference often struggle with complexities arising from high-dimensional data, especially when estimating individual treatment effects (ITE). CausalBGM emerges as a robust solution, innovatively integrating Bayesian methodologies with generative modeling to effectively mitigate confounding effects and provide comprehensive uncertainty quantification in causal estimates.

Key strengths of CausalBGM lie in its design principles and methodological approach. Central to its framework is the decomposition of high-dimensional data into interpretable low-dimensional latent structures that serve as confounders influencing both treatment and outcome variables. CausalBGM explicitly models the relationship among covariates, treatment, and outcome variables by employing a Bayesian network architecture, which inherently accommodates the estimation of probabilistic distributions instead of deterministic mappings. This allows CausalBGM to offer robust statistical rigor in the presence of uncertainty, setting it apart from previous AI-based methods that often focus on point estimates.

Moreover, CausalBGM introduces a novel iterative algorithm for training and updating model parameters and latent feature distributions, which supports scalability to large datasets. This iterative mechanism leverages mini-batch likelihood estimates and parallelizes computation across individuals, enhancing the framework's efficiency.

The paper highlights the empirical advantages of CausalBGM through extensive experiments demonstrating its superior predictive accuracy over state-of-the-art methods across both continuous and binary treatment settings. The framework is particularly notable for its improved performance in scenarios with non-linear relationships in the data, where it consistently achieves lower errors in estimating average dose-response functions and treatment effects. Additionally, CausalBGM provides well-calibrated posterior intervals, offering reliable measures of uncertainty in its estimates, which is critical for applications in personalized medicine and other fields requiring individualized causal inference.

Further distinguishing CausalBGM is its effective initialization strategy via Encoding Generative Modeling (EGM), which significantly enhances the convergence and stability of the estimation process. This strategy ensures that the latent space is meaningfully structured to capture the underlying causal dynamics of the observed data.

Despite its strengths, the paper identifies opportunities for extending CausalBGM's capabilities, such as exploring alternative initialization techniques to reduce potential sensitivity and enhancing theoretical insights into its convergence properties. Addressing these aspects will further bolster the framework's robustness and applicability in a broader range of complex causal inference tasks.

In conclusion, CausalBGM represents a significant advancement in the intersection of AI and causal inference, providing a scalable, interpretable, and statistically rigorous framework for addressing the challenges posed by high-dimensional observational data. Its integration of Bayesian generative modeling with AI principles facilitates credible causal effect estimation, positioning CausalBGM as a promising tool for modern data-centric applications in genomics, healthcare, and the social sciences.