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Sharp bounds on the causal effects in randomized experiments with "truncation-by-death"

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  • Imai, Kosuke
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    Abstract

    Many randomized experiments suffer from the "truncation-by-death" problem where potential outcomes are not defined for some subpopulations. For example, in medical trials, quality-of-life measures are only defined for surviving patients. In this article, I derive the sharp bounds on causal effects under various assumptions. My identification analysis is based on the idea that the "truncation-by-death" problem can be formulated as the contaminated data problem. The proposed analytical techniques can be applied to other settings in causal inference including the estimation of direct and indirect effects and the analysis of three-arm randomized experiments with noncompliance.

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    File URL: http://www.sciencedirect.com/science/article/B6V1D-4NYJ0M2-2/2/7875e6e828c065c788594f38b34092d0
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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 78 (2008)
    Issue (Month): 2 (February)
    Pages: 144-149

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    Handle: RePEc:eee:stapro:v:78:y:2008:i:2:p:144-149

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    Related research

    Keywords: Average treatment effect Causal inference Direct and indirect effect Identification Principal stratification Quantile treatment effect;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. 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.
    2. Jing Cheng & Dylan S. Small, 2006. "Bounds on causal effects in three-arm trials with non-compliance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(5), pages 815-836.
    3. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
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    Cited by:
    1. Martin Huber & Giovanni Mellace, 2010. "Sharp IV bounds on average treatment effects under endogeneity and noncompliance," University of St. Gallen Department of Economics working paper series 2010 2010-31, Department of Economics, University of St. Gallen.

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