Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data
This addresses the problem of confounding in psychiatric treatment effect estimation, offering a novel approach for clinicians and researchers, though it is incremental in improving existing causal inference methods.
The paper tackles the challenge of causal inference in psychiatric longitudinal data by optimizing outcome definitions to maximize causal identifiability, resulting in the DEBIAS algorithm that consistently outperforms state-of-the-art methods in recovering causal effects for depression and schizophrenia.
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.