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arXiv · 2508.17207

Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)

Xinyu Qin·Mark H. Chignell·Alexandria Greifenberger·Sachinthya Lokuge·Elssa Toumeh·Tia Sternat·Martin Katzman·Lu Wang
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Abstract

Background: This study investigates how variations in Major Depressive Disorder (MDD) symptoms, quantified by the Hamilton Rating Scale for Depression (HAM-D), causally influence the prescription of SSRIs versus SNRIs. Methods: We applied explainable counterfactual reasoning with counterfactual explanations (CFs) to assess the impact of specific symptom changes on antidepressant choice. Results: Among 17 binary classifiers, Random Forest achieved highest performance (accuracy, F1, precision, recall, ROC-AUC near 0.85). Sample-based CFs revealed both local and global feature importance of individual symptoms in medication selection. Conclusions: Counterfactual reasoning elucidates which MDD symptoms most strongly drive SSRI versus SNRI selection, enhancing interpretability of AI-based clinical decision support systems. Future work should validate these findings on more diverse cohorts and refine algorithms for clinical deployment.

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Xinyu Qin, Mark H. Chignell, Alexandria Greifenberger, Sachinthya Lokuge, Elssa Toumeh, Tia Sternat, Martin Katzman, Lu Wang. 2025-08-24. Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population). https://arxiv.org/abs/2508.17207

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