MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability

2025-04-29

AI Summary

In personalized medicine, identifying clinically actionable treatment-benefit subgroups from observational data faces dual challenges: accurately estimating heterogeneous treatment effects (HTE) while satisfying multiple practical constraints—including minimum subgroup size and adequate covariate balance among confounders—simultaneously. Existing methods struggle to achieve both objectives. This paper proposes the first model-agnostic, unified framework for multi-constrained optimal subgroup identification. It reformulates the combinatorial optimization problem as a provably convergent unconstrained min-max program and solves it via a gradient descent-ascent algorithm. The framework is compatible with arbitrary causal effect estimators and optimization techniques. Experiments on synthetic and real-world datasets demonstrate substantial improvements in HTE estimation accuracy and confounder balance quality, while robustly satisfying all specified constraints. By bridging statistical significance and clinical feasibility, our approach advances subgroup discovery toward actionable clinical decision support.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Identifying subgroups that benefit from specific treatments using observational data is a critical challenge in personalized medicine. Most existing approaches solely focus on identifying a subgroup with an improved treatment effect. However, practical considerations, such as ensuring a minimum subgroup size for representativeness or achieving sufficient confounder balance for reliability, are also important for making findings clinically meaningful and actionable. While some studies address these constraints individually, none offer a unified approach to handle them simultaneously. To bridge this gap, we propose a model-agnostic framework for optimal subgroup identification under multiple constraints. We reformulate this combinatorial problem as an unconstrained min-max optimization problem with novel modifications and solve it by a gradient descent ascent algorithm. We further prove its convergence to a feasible and locally optimal solution. Our method is stable and highly flexible, supporting various models and techniques for estimating and optimizing treatment effectiveness with observational data. Extensive experiments on both synthetic and real-world datasets demonstrate its effectiveness in identifying subgroups that satisfy multiple constraints, achieving higher treatment effects and better confounder balancing results across different group sizes.

Problem

• Identifying treatment-beneficial subgroups from observational data
• Ensuring subgroup size and confounder balance for reliability
• Unified framework for multi-constraint optimal subgroup identification

Innovation

• Model-agnostic framework for optimal subgroup identification
• Unconstrained min-max optimization with gradient descent ascent
• Flexible support for various treatment effectiveness models