Robust Bayesian causal estimation for causal inference in medical diagnosis

Basu, Tathagata; Troffaes, Matthias C. M.

Abstract

Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment. In a regressional framework, we assign a treatment and outcome model to estimate the average causal effect. Additionally, for high dimensional regression problems, variable selection methods are also used to find a subset of predictor variables that maximises the predictive performance of the underlying model for better estimation of the causal effect. In this paper, we propose a different approach. We focus on the variable selection aspects of high dimensional causal estimation problem. We suggest a cautious Bayesian group LASSO (least absolute shrinkage and selection operator) framework for variable selection using prior sensitivity analysis. We argue that in some cases, abstaining from selecting (or, rejecting) a predictor is beneficial and we should gather more information to obtain a more decisive result. We also show that for problems with very limited information, expert elicited variable selection can give us a more stable causal effect estimation as it avoids overfitting. Lastly, we carry a comparative study with synthetic dataset and show the applicability of our method in real-life situations.

Citation

Basu, T., & Troffaes, M. C. M. (2025). Robust Bayesian causal estimation for causal inference in medical diagnosis. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 177, Article 109330. https://doi.org/10.1016/j.ijar.2024.109330

• Journal Article Type: Article
• Acceptance Date: Nov 18, 2024
• Online Publication Date: Nov 22, 2024
• Publication Date: 2025-02
• Deposit Date: Nov 26, 2024
• Publicly Available Date: Nov 26, 2024
• Journal: International Journal of Approximate Reasoning
• Print ISSN: 0888-613X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 177
• Article Number: 109330
• DOI: https://doi.org/10.1016/j.ijar.2024.109330
• Public URL: https://durham-repository.worktribe.com/output/3106497
• Related Public URLs: https://arxiv.org/abs/2411.12477