Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management

• URL: http://arxiv.org/abs/2509.09655v1
• Date: Thu, 11 Sep 2025 17:50:06 GMT
• Title: Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management
• Authors: Sanjay Basu, Sadiq Y. Patel, Parth Sheth, Bhairavi Muralidharan, Namrata Elamaran, Aakriti Kinra, Rajaie Batniji,
• Abstract summary: We introduce Feasibility-Guided Fair Adaptive Reinforcement Learning (FG-FARL)<n>FG-FARL calibrates per-group safety thresholds to reduce harm while equalizing a chosen fairness target (coverage or harm) across protected subgroups.
• Score: 1.5635627702544692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Feasibility-Guided Fair Adaptive Reinforcement Learning (FG-FARL), an offline RL procedure that calibrates per-group safety thresholds to reduce harm while equalizing a chosen fairness target (coverage or harm) across protected subgroups. Using de-identified longitudinal trajectories from a Medicaid population health management program, we evaluate FG-FARL against behavior cloning (BC) and HACO (Hybrid Adaptive Conformal Offline RL; a global conformal safety baseline). We report off-policy value estimates with bootstrap 95% confidence intervals and subgroup disparity analyses with p-values. FG-FARL achieves comparable value to baselines while improving fairness metrics, demonstrating a practical path to safer and more equitable decision support.

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