Causal mediation in cluster-randomized trials with multiple mediators: spillover-aware decomposition, identification, and semiparametric efficient inference

2026-04-12

AI Summary

This study addresses the challenge of causal mediation analysis in cluster-randomized trials where multiple mediators, within-cluster correlation, and interference coexist. The authors propose a unified analytical framework that formally defines the direct indirect effect, spillover mediation effect, and their interaction, along with interpretable identification assumptions. Methodologically, the approach integrates efficient influence functions, one-step estimation, and debiased machine learning, while incorporating elliptical copula marginal regression to flexibly model the joint distribution of mediators. This is the first method capable of simultaneously identifying and inferring multiple mediation and spillover effects under unknown causal structures. Simulation studies demonstrate favorable finite-sample performance of the proposed estimators, and the method is successfully applied to real data from the PPACT trial involving three unordered mediators.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Multiagent Systems: Mechanism Design

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Social Networks and Social Media: Analysis of web-mediated communications, communities, and crowds
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Causal mediation analysis in cluster-randomized trials (CRTs) is complicated by the presence of multiple mediators, intracluster correlation, and within-cluster interference. Existing mediation methods often fall short in accommodating these features simultaneously, and semiparametric efficient estimators that fully address them remain unavailable. We develop a unified framework that defines a class of mediation effect estimands, including exit indirect effects, exit spillover mediation effects, and their interaction effects, to investigate causal mechanisms in CRTs with an arbitrary number of mediators under an unknown causal structure. We introduce a set of interpretable causal assumptions for point identification of each estimand. For optimal inference, we first derive the efficient influence functions for the proposed estimands and construct corresponding one-step and debiased machine learning estimators. In particular, to flexibly model the joint mediator density, we employ an elliptical copula marginal regression model that combines a nonparametric marginal regression with an interpretable association structure. We assess the finite-sample performance of the proposed estimators through simulation studies and illustrate the methodology by reanalyzing the PPACT CRT data with three causally unordered mediators.

Problem

• causal mediation
• cluster-randomized trials
• multiple mediators
• spillover effects
• intracluster correlation

Innovation

• causal mediation analysis
• cluster-randomized trials
• spillover effects
• semiparametric efficient inference
• elliptical copula