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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.09655 |
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| _version_ | 1866911150434680832 |
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| author | Basu, Sanjay Patel, Sadiq Y. Sheth, Parth Muralidharan, Bhairavi Elamaran, Namrata Kinra, Aakriti Batniji, Rajaie |
| author_facet | Basu, Sanjay Patel, Sadiq Y. Sheth, Parth Muralidharan, Bhairavi Elamaran, Namrata Kinra, Aakriti Batniji, Rajaie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management Basu, Sanjay Patel, Sadiq Y. Sheth, Parth Muralidharan, Bhairavi Elamaran, Namrata Kinra, Aakriti Batniji, Rajaie Machine Learning Artificial Intelligence Logic in Computer Science Applications 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. |
| title | Feasibility-Guided Fair Adaptive Offline Reinforcement Learning for Medicaid Care Management |
| topic | Machine Learning Artificial Intelligence Logic in Computer Science Applications |
| url | https://arxiv.org/abs/2509.09655 |