Identification and efficient estimation of compliance and network causal effects in cluster-randomized trials

2025-12-18

AI Summary

In infectious disease cluster-randomized trials, noncompliance induces heterogeneity in actual treatment receipt, confounding the complier-average causal effect (CACE) with network assignment effects—i.e., the pure impact of intervention assignment and spillovers within clusters. To address this, we propose the first structural assumption enabling nonparametric point identification of both effects. We develop a semiparametrically efficient estimation framework grounded in the efficient influence function and adaptive machine learning (e.g., TMLE, SuperLearner), jointly modeling principal strata and network spillovers. We further introduce sensitivity analysis to assess robustness under violation of key assumptions. Applied to a large-scale school-based deworming trial in Kenya, our approach substantially improves precision in causal effect decomposition and strengthens inferential robustness. This advances rigorous causal inference for public health policy evaluation by delivering more reliable, interpretable, and policy-relevant evidence.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Treatment noncompliance is pervasive in infectious disease cluster-randomized trials. Although all individuals within a cluster are assigned the same treatment condition, the treatment uptake status may vary across individuals due to noncompliance. We propose a semiparametric framework to evaluate the individual compliance effect and network assignment effect within principal stratum exhibiting different patterns of noncompliance. The individual compliance effect captures the portion of the treatment effect attributable to changes in treatment receipt, while the network assignment effect reflects the pure impact of treatment assignment and spillover among individuals within the same cluster. Unlike prior efforts which either empirically identify or interval identify these estimands, we characterize new structural assumptions for nonparametric point identification. We then develop semiparametrically efficient estimators that combine data-adaptive machine learning methods with efficient influence functions to enable more robust inference. Additionally, we introduce sensitivity analysis methods to study the impact under assumption violations, and apply the proposed methods to reanalyze a cluster-randomized trial in Kenya that evaluated the impact of school-based mass deworming on disease transmission.

Problem

• Estimates individual compliance and network effects in cluster-randomized trials
• Develops efficient semiparametric estimators using machine learning
• Provides sensitivity analysis for assumption violations in causal inference

Innovation

• Semiparametric framework for compliance and network effects
• Nonparametric point identification via new structural assumptions
• Efficient estimators with machine learning and influence functions