Causal Inference for Preprocessed Outcomes with an Application to Functional Connectivity

2026-02-02

AI Summary

This study addresses the lack of a systematic causal inference framework for within-subject pre-treatment derived outcomes in biomedical research by proposing a semi-parametric, modular causal inference approach tailored to the “within-subject treatment–between-subject analysis” setting, with a specific focus on natural direct effects in mediation analysis. The method integrates semi-parametric modeling, flexible machine learning algorithms, and multiply robust estimation to construct estimators that are robust to misspecification in both within- and between-subject models. To enable high-dimensional inference, it further incorporates a step-down procedure that controls the exceedance rate of the false discovery proportion. Extensive simulations demonstrate its superior performance, and the approach is successfully applied to assess the impact of stimulant medication on functional brain connectivity in children with autism spectrum disorder.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• causal inference
• preprocessed outcomes
• intra-subject processing
• derived outcomes
• functional connectivity

Innovation

• causal inference
• derived outcomes
• multiply robust estimation
• mediation analysis
• high-dimensional inference