MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability
This addresses a practical limitation in clinical decision-making by enabling more reliable subgroup identification, though it is incremental as it builds on existing CATE estimation methods.
The paper tackled the problem of identifying optimal subgroups in clinical decision-making by proposing a unified optimization framework that directly incorporates constraints like subgroup size and propensity overlap, resulting in improved constraint satisfaction and high-benefit subgroup identification in experiments.
Current subgroup identification methods typically follow a two-step approach: first estimate conditional average treatment effects and then apply thresholding or rule-based procedures to define subgroups. While intuitive, this decoupled approach fails to incorporate key constraints essential for real-world clinical decision-making, such as subgroup size and propensity overlap. These constraints operate on fundamentally different axes than CATE estimation and are not naturally accommodated within existing frameworks, thereby limiting the practical applicability of these methods. We propose a unified optimization framework that directly solves the primal constrained optimization problem to identify optimal subgroups. Our key innovation is a reformulation of the constrained primal problem as an unconstrained differentiable min-max objective, solved via a gradient descent-ascent algorithm. We theoretically establish that our solution converges to a feasible and locally optimal solution. Unlike threshold-based CATE methods that apply constraints as post-hoc filters, our approach enforces them directly during optimization. The framework is model-agnostic, compatible with a wide range of CATE estimators, and extensible to additional constraints like cost limits or fairness criteria. Extensive experiments on synthetic and real-world datasets demonstrate its effectiveness in identifying high-benefit subgroups while maintaining better satisfaction of constraints.