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Reduced Sensitivity to Hidden Bias at Upper Quantiles in Observational Studies with Dilated Treatment Effects

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  • Paul R. Rosenbaum

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  • Paul R. Rosenbaum, 1999. "Reduced Sensitivity to Hidden Bias at Upper Quantiles in Observational Studies with Dilated Treatment Effects," Biometrics, The International Biometric Society, vol. 55(2), pages 560-564, June.
    • Handle: RePEc:bla:biomet:v:55:y:1999:i:2:p:560-564
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.1999.00560.x
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    Cited by:

    1. Haensch, Anna-Carolina & Drechsler, Jörg & Bernhard, Sarah, 2020. "TippingSens: An R Shiny Application to Facilitate Sensitivity Analysis for Causal Inference Under Confounding," IAB-Discussion Paper 202029, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Peng Ding & Avi Feller & Luke Miratrix, 2016. "Randomization inference for treatment effect variation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 655-671, June.
    3. Zhao, Anqi & Ding, Peng, 2021. "Covariate-adjusted Fisher randomization tests for the average treatment effect," Journal of Econometrics, Elsevier, vol. 225(2), pages 278-294.
    4. Jing Cheng & Dylan S. Small, 2021. "Semiparametric models and inference for the effect of a treatment when the outcome is nonnegative with clumping at zero," Biometrics, The International Biometric Society, vol. 77(4), pages 1187-1201, December.

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