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

Sanjay Basu; Sadiq Y. Patel; Parth Sheth; Bhairavi Muralidharan; Namrata Elamaran; Aakriti Kinra; Rajaie Batniji

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arXiv · 2509.09655

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

Sanjay Basu·Sadiq Y. Patel·Parth Sheth·Bhairavi Muralidharan·Namrata Elamaran·Aakriti Kinra·Rajaie Batniji

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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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Sanjay Basu, Sadiq Y. Patel, Parth Sheth, Bhairavi Muralidharan, Namrata Elamaran, Aakriti Kinra, Rajaie Batniji. 2025-09-11. Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management. https://arxiv.org/abs/2509.09655

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