Causal Fairness Assessment of Treatment Allocation with Electronic Health Records
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866910288790421504 |
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| author | Zhang, Linying Richter, Lauren R. Wang, Yixin Ostropolets, Anna Elhadad, Noemie Blei, David M. Hripcsak, George |
| author_facet | Zhang, Linying Richter, Lauren R. Wang, Yixin Ostropolets, Anna Elhadad, Noemie Blei, David M. Hripcsak, George |
| contents | Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While various fairness metrics have emerged to assess fairness in decision-making processes, a growing focus has been on causality-based fairness concepts due to their capacity to mitigate confounding effects and reason about bias. However, the application of causal fairness notions in evaluating the fairness of clinical decision-making with electronic health record (EHR) data remains an understudied domain. This study aims to address the methodological gap in assessing causal fairness of treatment allocation with electronic health records data. We propose a causal fairness algorithm to assess fairness in clinical decision-making. Our algorithm accounts for the heterogeneity of patient populations and identifies potential unfairness in treatment allocation by conditioning on patients who have the same likelihood to benefit from the treatment. We apply this framework to a patient cohort with coronary artery disease derived from an EHR database to evaluate the fairness of treatment decisions. In addition, we investigate the impact of social determinants of health on the assessment of causal fairness of treatment allocation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_11183 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Causal Fairness Assessment of Treatment Allocation with Electronic Health Records Zhang, Linying Richter, Lauren R. Wang, Yixin Ostropolets, Anna Elhadad, Noemie Blei, David M. Hripcsak, George Machine Learning Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While various fairness metrics have emerged to assess fairness in decision-making processes, a growing focus has been on causality-based fairness concepts due to their capacity to mitigate confounding effects and reason about bias. However, the application of causal fairness notions in evaluating the fairness of clinical decision-making with electronic health record (EHR) data remains an understudied domain. This study aims to address the methodological gap in assessing causal fairness of treatment allocation with electronic health records data. We propose a causal fairness algorithm to assess fairness in clinical decision-making. Our algorithm accounts for the heterogeneity of patient populations and identifies potential unfairness in treatment allocation by conditioning on patients who have the same likelihood to benefit from the treatment. We apply this framework to a patient cohort with coronary artery disease derived from an EHR database to evaluate the fairness of treatment decisions. In addition, we investigate the impact of social determinants of health on the assessment of causal fairness of treatment allocation. |
| title | Causal Fairness Assessment of Treatment Allocation with Electronic Health Records |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2211.11183 |