Work

Conformal Causal Inference for Cluster Randomized Trials: Model-robust Inference Without Asymptotic Approximations

Year: 2026

Type: article

Abstract: Traditional statistical inference in cluster randomized trials typically invokes the asymptotic theory that requires the number of clusters to approach infinity.In this article, we propose an alternative conformal causal inference framework for analyzing cluster randomized trials that achieves the target inferential goal in finite samples without the need for asymptotic approximations.Different from traditional inference focusing on estimating the average treatment effect, our conformal causal inference aims to provide prediction intervals for the difference of counterfactual outcomes, thereby providing a new decision-making tool for clusters and individuals in the same target population.We prove that this framework is compatible with arbitrary working outcome models-including

Source: Statistica Sinica

DOI: 10.5705/ss.202025.0476

Authors: Bingkai Wang, Fan Li, Mengxin Yu

Institutions: University of Michigan, Yale University, Washington University in St. Louis

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

Funders: Patient-Centered Outcomes Research Institute, National Institutes of Health, National Institute of Allergy and Infectious Diseases

Awards: G4818794375, Improving the design and statistical analysis of cluster-randomized trials on tropical infectious diseases