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

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  • Tat Y. Chan
  • Barton H. Hamilton

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.

Suggested Citation

  • Tat Y. Chan & Barton H. Hamilton, 2006. "Learning, Private Information, and the Economic Evaluation of Randomized Experiments," Journal of Political Economy, University of Chicago Press, vol. 114(6), pages 997-1040, December.
  • Handle: RePEc:ucp:jpolec:v:114:y:2006:i:6:p:997-1040
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    References listed on IDEAS

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    1. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    2. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    3. James Heckman & Neil Hohmann & Jeffrey Smith & Michael Khoo, 2000. "Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment," The Quarterly Journal of Economics, Oxford University Press, vol. 115(2), pages 651-694.
    4. Anup Malani, 2006. "Identifying Placebo Effects with Data from Clinical Trials," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 236-256, April.
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    Cited by:

    1. Ganglmair, Bernhard & Simcoe, Timothy & Tarantino, Emanuele, 2018. "Learning When to Quit: An Empirical Model of Experimentation," CEPR Discussion Papers 12733, C.E.P.R. Discussion Papers.
    2. 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).
    3. Chemla, Gilles & Hennessy, Christopher, 2016. "Bayesian Expectancy Invalidates Double-Blind Randomized Controlled Medical Trials," CEPR Discussion Papers 11360, C.E.P.R. Discussion Papers.
    4. 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.
    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. Michael Darden, 2017. "Smoking, Expectations, and Health: A Dynamic Stochastic Model of Lifetime Smoking Behavior," Journal of Political Economy, University of Chicago Press, vol. 125(5), pages 1465-1522.
    7. Jose M. Fernandez, 2013. "An Empirical Model Of Learning Under Ambiguity: The Case Of Clinical Trials," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 54(2), pages 549-573, May.
    8. John Mullahy, 2017. "Individual Results May Vary: Elementary Analytics of Inequality-Probability Bounds, with Applications to Health-Outcome Treatment Effects," NBER Working Papers 23603, National Bureau of Economic Research, Inc.
    9. 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.
    10. Maurer, J. & Harris, K.M., 2015. "Learning to trust flu shots: quasi-experimental evidence on the role of learning in influenza vaccination decisions from the 2009 influenza A/H1N1 (swine flu) pandemic," Health, Econometrics and Data Group (HEDG) Working Papers 15/19, HEDG, c/o Department of Economics, University of York.

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