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

TL;DR

This paper proposes a new decomposition method to explain and quantify heterogeneity in causal effects across different studies, enhancing understanding in causal inference for observational and randomized research.

Contribution

It introduces a formal decomposition framework that identifies sources of variability in treatment effects across studies, improving estimation robustness and interpretability.

Findings

Validated through simulation studies.

Applied to Moving to Opportunity data.

Demonstrated practical relevance in causal analysis.

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.