# Estimation and Sensitivity Analysis for Causal Decomposition in Heath Disparity Research

arXiv: https://arxiv.org/abs/2008.12812 · HTML: https://mlsanity.com/paper/2008.12812/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Practical Guide for Causal Pathways and Sub-group Disparity Analysis](https://mlsanity.com/paper/2407.02702/) (2024)
- [Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities](https://mlsanity.com/paper/2506.18994/) (2025)
- [Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions](https://mlsanity.com/paper/2506.19010/) (2025)

## Abstract (arXiv)

In the field of disparities research, there has been growing interest in developing a counterfactual-based decomposition analysis to identify underlying mediating mechanisms that help reduce disparities in populations. Despite rapid development in the area, most prior studies have been limited to regression-based methods, undermining the possibility of addressing complex models with multiple mediators and/or heterogeneous effects. We propose an estimation method that effectively addresses complex models. Moreover, we develop a novel sensitivity analysis for possible violations of identification assumptions. The proposed method and sensitivity analysis are demonstrated with data from the Midlife Development in the US study to investigate the degree to which disparities in cardiovascular health at the intersection of race and gender would be reduced if the distributions of education and perceived discrimination were the same across intersectional groups.