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

TL;DR

This paper develops a simulation-based sensitivity analysis and benchmarking approach to assess and quantify the impact of unmeasured confounding in causal decomposition analyses involving individualized treatment effects and optimal treatment regimes.

Contribution

It introduces a novel simulation-based sensitivity analysis and a formal benchmarking strategy for binary risk factors, addressing confounding issues in personalized causal inference.

Findings

Demonstrated the methods using the HSLS:09 dataset

Provided a way to quantify the strength of unmeasured confounding

Extended existing sensitivity analysis techniques for individualized effects

Abstract

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.