Robust Bayesian causal estimation for causal inference in medical diagnosis

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Basu, Tathagata and Troffaes, Matthias C.M. (2025) Robust Bayesian causal estimation for causal inference in medical diagnosis. International Journal of Approximate Reasoning, 177. 109330. ISSN 0888-613X (https://doi.org/10.1016/j.ijar.2024.109330)

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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.

ORCID iDs

Basu, Tathagata ORCID logoORCID: https://orcid.org/0000-0002-6851-154X and Troffaes, Matthias C.M.;

Persistent Identifier

https://doi.org/10.17868/strath.00091259
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