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

2025-06-23

AI Summary

This paper addresses estimation bias in optimal treatment regimes (OTRs) and causal decomposition under individualized interventions, arising from omitted binary confounders. We propose a simulation-based sensitivity analysis framework that systematically simulates unobserved confounders for individualized causal effect estimation—a first in this domain—and introduces interpretable, benchmarked metrics to quantify the magnitude of bias induced by such omissions on binary risk factor effects. Our contributions are twofold: (1) we establish the first formal benchmarking method for confounding strength specifically tailored to binary exposures; and (2) we enable bias correction and enhanced robustness in causal decomposition under individualized policies. Empirical validation using the HSLS:09 dataset demonstrates that the framework effectively identifies, quantifies, and mitigates confounding bias, thereby substantially improving the reliability of policy-relevant causal inferences.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Assess modifying risk factors to reduce social disparities
• Develop sensitivity analysis for omitted confounding in OTRs
• Propose benchmarking for omitted confounding in binary risk factors

Innovation

• Simulation-based sensitivity analysis for unmeasured confounders
• Formal bounding strategy for binary risk factors
• Individualized interventions with optimal treatment regimes