In biomedical research, repeated measurements within each subject are often processed to remove artifacts and unwanted sources of variation. The resulting data are used to construct derived outcomes that act as proxies for scientific outcomes that are not directly observable. Although intra-subject processing is widely used, its impact on inter-subject statistical inference has not been systematically studied, and a principled framework for causal analysis in this setting is lacking. In this article, we propose a semiparametric framework for causal inference with derived outcomes obtained after intra-subject processing. This framework applies to settings with a modular structure, where intra-subject analyses are conducted independently across subjects and are followed by inter-subject analyses based on parameters from the intra-subject stage. We develop multiply robust estimators of causal parameters under rate conditions on both intra-subject and inter-subject models, which allows the use of flexible machine learning. We specialize the framework to a mediation setting and focus on the natural direct effect. For high dimensional inference, we employ a step-down procedure that controls the exceedance rate of the false discovery proportion. Simulation studies demonstrate the superior performance of the proposed approach. We apply our method to estimate the impact of stimulant medication on brain connectivity in children with autism spectrum disorder.
Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)
Tulane University（杜兰大学）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；Mind Research Network（心智研究网络）；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Functional Linear Non-Gaussian Acyclic Model for Causal Discovery
Graduate School of Engineering Science Osaka University（大阪大学工学研究科）；Department of Statistics, Operations, and Data Science（统计学、运营与数据科学系）；Machine Learning Department（机器学习系）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·拉希德智能大学）
Causal Graph Recovery in Neuroimaging through Answer Set Programming
Georgia Institute of Technology（佐治亚理工学院）；Georgia State University（佐治亚州立大学）；Center for Translational Research in Neuroimaging and Data Science (TReNDS)（神经影像与数据科学转化研究中心）；University of California San Diego（加州大学圣地亚哥分校）
