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Statistical analysis of randomized experiments with non-ignorable missing binary outcomes: an application to a voting experiment

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  • Kosuke Imai

Abstract

Missing data are frequently encountered in the statistical analysis of randomized experiments. I propose statistical methods that can be used to analyse randomized experiments with a non-ignorable missing binary outcome where the missing data mechanism may depend on the unobserved values of the outcome variable itself even after taking into account the information in the fully observed variables. The motivating empirical example is a German election experiment where researchers are worried that the act of voting may increase the probability of participation in the post-election survey through which the outcome variable, turnout, was measured. To address this problem, I first introduce an identification strategy for the average treatment effect under the non-ignorability assumption and compare it with the existing alternative approaches in the literature. I then derive the maximum likelihood estimator and its asymptotic distribution and discuss possible estimation methods. Furthermore, since the identification assumption proposed is not directly verifiable from the data, I show how to conduct a sensitivity analysis based on the parameterization that links the key identification assumption with the causal quantities of interest. Finally, the methodology proposed is extended to the analysis of randomized experiments with non-compliance. In addition, although the method that is introduced may not directly apply to randomized experiments with non-binary outcomes, I briefly discuss possible identification strategies in more general situations. Copyright (c) 2009 Royal Statistical Society.

Suggested Citation

  • Kosuke Imai, 2009. "Statistical analysis of randomized experiments with non-ignorable missing binary outcomes: an application to a voting experiment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 83-104.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:1:p:83-104
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9876.2008.00637.x
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    References listed on IDEAS

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    1. Amy L. Stubbendick & Joseph G. Ibrahim, 2003. "Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects Models," Biometrics, The International Biometric Society, vol. 59(4), pages 1140-1150, December.
    2. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    3. Kosuke Imai, 2005. "Do get-out-the-vote calls reduce turnout? The importance of statistical methods for field experiments," Natural Field Experiments 00272, The Field Experiments Website.
    4. repec:cup:apsrev:v:94:y:2000:i:03:p:653-663_22 is not listed on IDEAS
    5. repec:cup:apsrev:v:99:y:2005:i:02:p:283-300_05 is not listed on IDEAS
    6. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    7. Alan Gerber & Donald Green, 2000. "The effects of canvassing, direct mail, and telephone contact on voter turnout: A field experiment," Natural Field Experiments 00248, The Field Experiments Website.
    8. Burden, Barry C., 2000. "Voter Turnout and the National Election Studies," Political Analysis, Cambridge University Press, vol. 8(04), pages 389-398, July.
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    Cited by:

    1. Martin Huber, 2010. "Identification of average treatment effects in social experiments under different forms of attrition," University of St. Gallen Department of Economics working paper series 2010 2010-22, Department of Economics, University of St. Gallen.

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