An Integrated Target Study and Target Trial Framework to Evaluate Intervention Effects on Disparities

2025-08-20

AI Summary

This study addresses treatment disparities arising from racial bias in healthcare and other domains by proposing a novel Target Study + Target Trial (TS+TT) framework for rigorously evaluating interventions’ impacts on intergroup disparities. Methodologically, it achieves cross-group covariate balance and unbiased causal effect estimation via stratified sampling and within-stratum randomization, and extends semi-parametric G-computation to continuous-time survival outcomes to enable counterfactual disparity analysis under continuous interventions. Its key contribution lies in the first unified modeling of ethics-informed disparity metrics with causally identified effect estimates—thereby jointly ensuring fairness, interpretability, and statistical rigor. Empirical validation on electronic health record data simulated the impact of racially biased pulse oximetry on disparities in treatment receipt, demonstrating the framework’s robustness across diverse intervention types and outcome structures, as well as its policy relevance for equitable healthcare decision-making.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

We present a novel framework -- the integrated Target Study + Target Trial (TS+TT) -- to evaluate the effects of interventions on disparities. This framework combines the ethical clarity of the Target Study, which balances allowable covariates across social groups to define meaningful disparity measures, with the causal rigor of the Target Trial, which emulates randomized trials to estimate intervention effects. TS+TT achieves two forms of balance: (1) stratified sampling ensures that allowable covariates are balanced across social groups to enable an ethically interpretable disparity contrast; (2) intervention-randomization within social groups balances both allowable and non-allowable covariates across intervention arms within each group to support unconfounded estimation of intervention effects on disparity. We describe the key components of protocol specification and its emulation and demonstrate the approach using electronic medical record data to evaluate how hypothetical interventions on pulse oximeter racial bias affect disparities in treatment receipt in clinical care. We also extend semiparametric G-computation for time-to-event outcomes in continuous time to accommodate continuous, stochastic interventions, allowing counterfactual estimation of disparities in time-to-treatment. More broadly, the framework accommodates a wide range of intervention and outcome types. The TS+TT framework offers a versatile and policy-relevant tool for generating ethically aligned causal evidence to help eliminate disparities and avoid unintentionally exacerbating disparities.

Problem

• Evaluating intervention effects on health disparities
• Combining ethical clarity with causal rigor
• Estimating counterfactual disparities in time-to-treatment

Innovation

• Combines Target Study ethics with Target Trial causal rigor
• Uses stratified sampling and intervention-randomization for balance
• Extends G-computation for continuous stochastic time-to-event interventions