Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data

TL;DR

This paper introduces DEBIAS, a novel algorithm for causal inference in psychiatric longitudinal data that optimizes outcome definitions to improve identifiability and reduce confounding, outperforming existing methods.

Contribution

The paper proposes a new method that directly optimizes outcome aggregation to enhance causal effect estimation in complex psychiatric data.

Findings

DEBIAS outperforms state-of-the-art methods in experiments

It provides an empirically verifiable test for unconfoundedness

The method improves causal effect recovery for depression and schizophrenia outcomes

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.