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Learning, Private Information, and the Economic Evaluation of Randomized Experiments

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  • Tat Y. Chan
  • Barton H. Hamilton
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    Abstract

    Many randomized experiments are plagued by attrition, even among subjects receiving more effective treatments. We estimate the subject's utility associated with the receipt of treatment, as revealed by dropout behavior, to evaluate treatment effects. Utility is a function of both "publicly observed" outcomes and side effects privately observed by the subject. We analyze an influential AIDS clinical trial, ACTG 175, and show that for many subjects, AZT yields the highest level of utility despite having the smallest impact on the publicly observed outcome because of mild side effects. Moreover, although subjects enter the experiment uncertain of treatment effectiveness (and often the treatment received), the learning process implies that early dropout in ACTG 175 is primarily driven by side effects, whereas later attrition reflects declining treatment effectiveness.

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    Bibliographic Info

    Article provided by University of Chicago Press in its journal Journal of Political Economy.

    Volume (Year): 114 (2006)
    Issue (Month): 6 (December)
    Pages: 997-1040

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    Handle: RePEc:ucp:jpolec:v:114:y:2006:i:6:p:997-1040

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    Web page: http://www.journals.uchicago.edu/JPE/

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    Cited by:
    1. Hu, Yingyao & Kayaba, Yutaka & Shum, Matthew, 2013. "Nonparametric learning rules from bandit experiments: The eyes have it!," Games and Economic Behavior, Elsevier, vol. 81(C), pages 215-231.
    2. Darden, M, 2010. "Smoking, Expectations, and Health: A Dynamic Stochastic Model of Lifetime Smoking Behavior," Health, Econometrics and Data Group (HEDG) Working Papers 10/28, HEDG, c/o Department of Economics, University of York.
    3. Fernandez, Jose, 2008. "An Empirical Model of Learning under Ambiguity: The Case of Clinical Trials," MPRA Paper 8621, University Library of Munich, Germany.
    4. van den Berg, Gerard J., 2007. "An Economic Analysis of Exclusion Restrictions for Instrumental Variable Estimation," IZA Discussion Papers 2585, Institute for the Study of Labor (IZA).
    5. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    6. Gilleskie, Donna, 2010. "Work absences and doctor visits during an illness episode: The differential role of preferences, production, and policies among men and women," Journal of Econometrics, Elsevier, vol. 156(1), pages 148-163, May.

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