# Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions

arXiv: https://arxiv.org/abs/2506.19010 · HTML: https://mlsanity.com/paper/2506.19010/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity](https://mlsanity.com/paper/1911.09260/) (2019)
- [Decomposition analysis to identify intervention targets for reducing disparities](https://mlsanity.com/paper/1703.05899/) (2017)
- [Meaningful causal decompositions in health equity research: definition, identification, and estimation through a weighting framework](https://mlsanity.com/paper/1909.10060/) (2019)
- [Estimation and Sensitivity Analysis for Causal Decomposition in Heath Disparity Research](https://mlsanity.com/paper/2008.12812/) (2020)
- [Sensitivity Analysis for Causal Decomposition Analysis: Assessing Robustness Toward Omitted Variable Bias](https://mlsanity.com/paper/2205.13127/) (2022)

## Abstract (arXiv)

Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the newly defined individualized effects rely on the no omitted confounding assumption, developing sensitivity analyses to account for potential omitted confounding is essential. Moreover, OTRs and individualized effects are primarily based on binary risk factors, and no formal approach currently exists to benchmark the strength of omitted confounding using observed covariates for binary risk factors. To address this gap, we extend a simulation-based sensitivity analysis that simulates unmeasured confounders, addressing two sources of bias emerging from deriving OTRs and estimating individualized effects. Additionally, we propose a formal bounding strategy that benchmarks the strength of omitted confounding for binary risk factors. Using the High School Longitudinal Study 2009 (HSLS:09), we demonstrate this sensitivity analysis and benchmarking method.