Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data

• URL: http://arxiv.org/abs/2506.16629v2
• Date: Mon, 30 Jun 2025 01:26:01 GMT
• Title: Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data
• Authors: Eric V. Strobl,
• Abstract summary: Causal inference in longitudinal biomedical data remains a central challenge.<n>Our algorithm learns non-negative, clinically interpretable weights for outcome aggregation.<n>Our algorithm consistently outperforms state-of-the-art methods in recovering causal effects.
• Score: 6.09170287691728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal inference in longitudinal biomedical data remains a central challenge, especially in psychiatry, where symptom heterogeneity and latent confounding frequently undermine classical estimators. Most existing methods for treatment effect estimation presuppose a fixed outcome variable and address confounding through observed covariate adjustment. However, the assumption of unconfoundedness may not hold for a fixed outcome in practice. To address this foundational limitation, we directly optimize the outcome definition to maximize causal identifiability. Our DEBIAS (Durable Effects with Backdoor-Invariant Aggregated Symptoms) algorithm learns non-negative, clinically interpretable weights for outcome aggregation, maximizing durable treatment effects and empirically minimizing both observed and latent confounding by leveraging the time-limited direct effects of prior treatments in psychiatric longitudinal data. The algorithm also furnishes an empirically verifiable test for outcome unconfoundedness. DEBIAS consistently outperforms state-of-the-art methods in recovering causal effects for clinically interpretable composite outcomes across comprehensive experiments in depression and schizophrenia.

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