Causal inference and explainable machine learning for analyzing treatment side effect in metastatic castration-resistant prostate cancer patients

Petinrin, Olutomilayo Olayemi and Saeed, Faisal and Xue, Hao and Basu, Sumanta and Basurra, Shadi and Liu, Zhe and Toseef, Muhammad and Muyide, Ibukun Omotayo and Wong, Ka-Chun (2026) Causal inference and explainable machine learning for analyzing treatment side effect in metastatic castration-resistant prostate cancer patients. Egyptian Informatics Journal, 33. p. 100895. ISSN 1110-8665

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Abstract

Optimal treatment recommendation for metastatic castration-resistant prostate cancer (mCRPC) are inherently diverse, being contingent upon individual patient response. Furthermore, treatment efficacy in specific patient cohorts can be influenced by confounding factors. Considering the substantial genetic heterogeneity among patients, generating population-level generalizations may compromise the precision and clinical applicability of predictive models. This study examines the prediction of treatment-induced adverse events in mCRPC patients using Explainable AI (XAI), focusing on both global and local levels of interpretability. Machine learning and other computational tools are often perceived as ”black-box” techniques, largely due to the challenge of linking their internal processes to the final model outputs. Consequently, XAI offers crucial insight into the specific features that the algorithms prioritize for prediction, thereby illuminating the opacity and decision-making intricacies of these ”black-box” models. Furthermore, causal inference was used to identify the attributes that specifically precipitate adverse events in patients with a smoking history. This analysis demonstrated that testosterone levels, prior analgesic use, and calcium levels act as confounders for adverse events within the smoking patients subgroup. The integration of causal inference and XAI establishes a robust and interpretable framework for making personalized treatment decisions in cancer care.

Item Type:	Article
Identification Number:	10.1016/j.eij.2026.100895
Dates:	Date Event 18 January 2026 Accepted 4 February 2026 Published Online
Uncontrolled Keywords:	Cancer metastasis, Causal inference, Explainable AI, Machine learning, Sensitivity analysis, Treatment effect
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	11 May 2026 14:03
Last Modified:	11 May 2026 14:03
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17030

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