通过优化症状组合方式，提升精神疾病纵向数据的因果推断效果。
Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data
- 设计DEBIAS算法，自动学习症状加权组合以增强因果可识别性。
- 在抑郁和精神分裂症数据中，显著提升对治疗效果的准确恢复能力。
- 提供可验证的无混杂性检验，适合临床研究与长期随访数据分析。
纵向生物医学数据中的因果推断仍是核心挑战，尤其在精神科领域，症状异质性和潜在混杂常导致经典估计方法失效。现有方法通常假设结果变量固定，并通过观测协变量调整来处理混杂，但实际中该无混杂假设可能不成立。为此，我们直接优化结果定义以最大化因果可识别性。提出的DEBIAS（Durable Effects with Backdoor-Invariant Aggregated Symptoms）算法学习非负且具临床可解释性的症状聚合权重，通过利用先前治疗在时间上的直接效应，最大化持久治疗效应，并实证上最小化可观测与潜在混杂。该算法还提供可实证验证的结果无混杂性检验。在抑郁症和精神分裂症的综合实验中，DEBIAS持续优于现有先进方法，在恢复具临床意义的复合结果因果效应方面表现卓越。
原文摘要 · Abstract (English)
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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