Model-Based Causal Feature Selection for General Response Types

• 成果类型: Article
• 署名作者: Kook, Lucas; Saengkyongam, Sorawit; Lundborg, Anton Rask; Hothorn, Torsten; Peters, Jonas
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Copenhagen; Swiss School of Public Health (SSPH+); University of Zurich
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2395588
• 发表日期: 2025
• 页码: 1090-1101
• 关键词: transformation models; markov equivalence; regression-models; inference; survival; collapsibility; RESIDUALS

摘要

Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters, B & uuml;hlmann, and Meinshausen) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor Type I error control) and additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but reflects categories or counts. Here, we develop transformation-model (tram) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses (these model classes, generally, do not allow for identifiability when there is no exogenous heterogeneity). As an invariance test, we propose tram-GCM based on the expected conditional covariance between environments and score residuals with uniform asymptotic level guarantees. For the special case of linear shift trams, we also consider tram-Wald, which tests invariance based on the Wald statistic. We provide an open-source R package tramicp and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.