Work

A novel decomposition to explain heterogeneity in observational and randomized studies of causality

Year: 2026

Type: article

Abstract: This paper introduces a novel decomposition framework to explain heterogeneity in causal effects observed across different studies, considering both observational and randomized settings. We present a formal decomposition of between-study heterogeneity, identifying sources of variability in treatment effects across studies. The proposed methodology allows for robust estimation of causal parameters under various assumptions, addressing differences in pre-treatment covariate distributions, mediating variables, and the outcome mechanism. Our approach is validated through a simulation study and applied to data from the Moving to Opportunity (MTO) study, demonstrating its practical relevance. This work contributes to the broader understanding of causal inference in multi-study environments, with potential applications in evidence synthesis and policy-making.

Source: Biostatistics

DOI: 10.1093/biostatistics/kxag030

Authors: Brian Gilbert, Iván Díaz, Kara Rudolph, Nicholas Williams, Tat-Thang Vo

Institutions: New York University, Columbia University, Université Paris-Est Créteil, Epidemiology in dermatology and evaluation of therapeutics

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: Patient-Centered Outcomes Research Institute

Award: G6189804798