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Randomization inference for treatment effect variation

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  • Peng Ding
  • Avi Feller
  • Luke Miratrix

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  • 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.
  • Handle: RePEc:bla:jorssb:v:78:y:2016:i:3:p:655-671
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    References listed on IDEAS

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    1. Paul R. Rosenbaum, 2011. "A New u-Statistic with Superior Design Sensitivity in Matched Observational Studies," Biometrics, The International Biometric Society, vol. 67(3), pages 1017-1027, September.
    2. Marianne P. Bitler & Jonah B. Gelbach & Hilary W. Hoynes, 2006. "What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments," American Economic Review, American Economic Association, vol. 96(4), pages 988-1012, September.
    3. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2005. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 735-765.
    4. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2008. "Nonparametric Tests for Treatment Effect Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 389-405, August.
    5. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    6. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    7. 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.
    8. Djebbari, Habiba & Smith, Jeffrey, 2008. "Heterogeneous impacts in PROGRESA," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 64-80, July.
    9. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    10. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    11. Nolen, Tracy L. & Hudgens, Michael G., 2011. "Randomization-Based Inference Within Principal Strata," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 581-593.
    12. Luke W. Miratrix & Jasjeet S. Sekhon & Bin Yu, 2013. "Adjusting treatment effect estimates by post-stratification in randomized experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 369-396, March.
    13. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
    14. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
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    Cited by:

    1. John Cai & Weinan Wang, 2022. "A Systematic Paradigm for Detecting, Surfacing, and Characterizing Heterogeneous Treatment Effects (HTE)," Papers 2211.01547, arXiv.org.
    2. Chung, EunYi & Olivares, Mauricio, 2021. "Permutation test for heterogeneous treatment effects with a nuisance parameter," Journal of Econometrics, Elsevier, vol. 225(2), pages 148-174.
    3. Peter Andre, 2022. "Shallow Meritocracy," CRC TR 224 Discussion Paper Series crctr224_2022_318v3, University of Bonn and University of Mannheim, Germany.
    4. Myers, Erica & Souza, Mateus, 2020. "Social comparison nudges without monetary incentives: Evidence from home energy reports," Journal of Environmental Economics and Management, Elsevier, vol. 101(C).
    5. Andre, Peter, 2023. "Shallow meritocracy," SAFE Working Paper Series 405, Leibniz Institute for Financial Research SAFE.
    6. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
    7. Kirk Bansak, 2021. "Estimating causal moderation effects with randomized treatments and non‐randomized moderators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 65-86, January.
    8. Paul Carrillo & Dave Donaldson & Dina Pomeranz & Monica Singhal, 2023. "Misallocation in Firm Production: A Nonparametric Analysis Using Procurement Lotteries," NBER Working Papers 31311, National Bureau of Economic Research, Inc.
    9. Monteiro Amaral,Sofia Fernando & Dinarte Diaz,Lelys Ileana & Dominguez,Patricio & Perez-Vincent,Santiago M. & Romero,Steffanny, 2022. "Talk or Text ? Evaluating Response Rates by Remote Survey Method during COVID-19," Policy Research Working Paper Series 9999, The World Bank.
    10. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
    11. Harvey, Matthew & Nickerson, David & Wozniak, Abigail, 2023. "When Fairness Matters: Cross-Race Responses to Intentionally Fair Treatment," IZA Discussion Papers 16582, Institute of Labor Economics (IZA).
    12. Yu, Jisang & Villoria, Nelson B. & Hendricks, Nathan P., 2022. "The incidence of foreign market tariffs on farmland rental rates," Food Policy, Elsevier, vol. 112(C).
    13. Peter Andre, 2021. "Shallow Meritocracy: An Experiment on Fairness Views," ECONtribute Discussion Papers Series 115, University of Bonn and University of Cologne, Germany.
    14. Haushofer, Johannes & Niehaus, Paul & Paramo, Carlos & Miguel, Edward & Walker, Michael W, 2022. "Targeting Impact Versus Deprivation," Department of Economics, Working Paper Series qt07j8n9vz, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    15. Zach Branson & Tirthankar Dasgupta, 2020. "Sampling‐based Randomised Designs for Causal Inference under the Potential Outcomes Framework," International Statistical Review, International Statistical Institute, vol. 88(1), pages 101-121, April.
    16. Julius Owusu, 2023. "Randomization Inference of Heterogeneous Treatment Effects under Network Interference," Papers 2308.00202, arXiv.org, revised Jan 2024.
    17. David Puelz & Guillaume Basse & Avi Feller & Panos Toulis, 2022. "A graph‐theoretic approach to randomization tests of causal effects under general interference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 174-204, February.
    18. Hyunseung Kang & Laura Peck & Luke Keele, 2018. "Inference for instrumental variables: a randomization inference approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1231-1254, October.
    19. Avi Feller & Fabrizia Mealli & Luke Miratrix, 2017. "Principal Score Methods: Assumptions, Extensions, and Practical Considerations," Journal of Educational and Behavioral Statistics, , vol. 42(6), pages 726-758, December.
    20. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.

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