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

arXiv: https://arxiv.org/abs/2208.05543 · HTML: https://mlsanity.com/paper/2208.05543/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2022.

## Built on by (most cited first, 1 of 1)

- [Towards Generalizing Inferences from Trials to Target Populations](https://mlsanity.com/paper/2402.17042/) (2024)

## Abstract (arXiv)

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.