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

  • Imai, Kosuke
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    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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    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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    1. 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.
    2. Alberto Abadie & Joshua Angrist & Guido Imbens, 1999. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Working papers 99-16, Massachusetts Institute of Technology (MIT), Department of Economics.
    3. Donald B. Rubin, 2004. "Direct and Indirect Causal Effects via Potential Outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 161-170.
    4. 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.
    5. 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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