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The distribution of health care payments to insurance plans has substantial consequences for social policy.
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McGuire, TG., Glazer, J., Newhouse, JP., Normand, SL., Shi, J., Sinaiko, AD., and Zuvekas, SH. (2013), “Integrating Risk Adjustment and Enrollee Premiums in Health Plan Payment,” Journal of Health Economics
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Kautter, J., Pope, GC., Ingber, M., Freeman, S., Patterson, L., Cohen, M., and Keenan, P. (2014), “The HHS-HCC Risk Adjustment Model for Individual and Small Group Markets under the Affordable Care Act,” Medicare & Medicaid Research Review
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Jacobs, DB. and Sommers, BD. (2015), “Using Drugs to Discriminate – Adverse Selection in the Insurance Marketplace,” NEJM
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Montz, E., Layton, TJ., Busch, AB., Ellis, RP., Rose, S., and McGuire, TG. (2016), “Risk adjustment simulation: Plans may have incentives to distort mental health and substance use coverage,” Health Affairs
2016
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Rose, S. (2016), “A Machine Learning Framework for Plan Payment Risk Adjustment,” Health Services Research
2016
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Dwork, C., Immorlica, N., Kalai, AT., and Leiserson, M. (2018), “Decoupled classifiers for fair and efficient machine learning,” Proceedings of Machine Learning Research
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Ellis, RP., Martins, B., and Rose, S. (2018), “Risk Adjustment for Health Plan Payment,” in Risk Adjustment, Risk Sharing and Premium Regulation in Health Insurance Markets: Theory and Practice
2018
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2018
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2017
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2017
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Ericson, KM., Geissler, K., and Lubin, B. (2017), “The Impact of Partial-Year Enrollment on the Accuracy of Risk Adjustment Systems: A Framework and Evidence,” NBER Working Paper #23765. nber.org/papers/w23765
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Geruso, M., Layton, TJ., and Prinz, D. (2017), “Screening in Contract Design: Evidence from the ACA Health insurance exchanges,” NBER Working Paper #22832. nber.org/papers/w22832
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2017
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Layton, TJ., Ellis, RP., McGuire, TG., and van Kleef, RC. (2017), “Measuring efficiency of health plan payment systems in managed competition health insurance markets,” Journal of Health Economics
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Rose, S., Bergquist, SL., and Layton, TJ. (2017), “Computational health economics for identification of unprofitable health care enrollees,” Biostatistics
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Fu, A., Narasimhan, B., and Boyd, S. (2018), “CVXR: An R Package for Discplined Convex Optimization,” web.stanford.edu/~boyd/papers/pdf/cvxr_paper.pdf
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Kleinberg, J., Ludwig, J., Mullainathan, S., and Rambachan, A. (2018), “Algorithmic Fairness,” AEA Papers and Proceedings
2018
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McGuire, T. and van Kleef, R. (eds.) (2018), Risk Adjustment, Risk Sharing and Premium Regulation in Health Insurance Markets
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Park, S. and Basu, A. (2018), “Alternative evaluation metrics for risk adjustment methods,” Health Economics
2018
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Shrestha, A., Bergquist, SL., Montz, E., and Rose, S. (2018), “Mental Health Risk Adjustment with Clinical Categories and Machine Learning,” Health Services Research
2018
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van Kleef, RC., van Vliet, RC., and van de Ven, WP. (2018), “Health plan payment in the Netherlands,” in Risk Adjustment, Risk Sharing and Premium Regulation in Health Insurance Markets: Theory and Practice
2018
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Withagen-Koster, AA., van Kleef, RC., and Eijkenaar, F. (2018), “Examining unpriced risk heterogeneity in the Dutch health insurance market,” The European Journal of Health Economics
2018
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Bergquist, SL., Layton, TJ., McGuire, TG., and Rose, S. (2019), “Data transformations to improve the performance of health plan payment methods,” Journal of Health Economics
2019
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Rose, S. and McGuire, TG. (2019), “Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment,” The American Statistician
2019
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