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Median treatment effect in randomized trials

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  • Myoung‐jae Lee

Abstract

For two response variables yt and yc corresponding to two treatments for two policies) T and C, we wish to learn about quantiles of yt−yc from the marginal quantiles of yt and yc; only one of yt and yc is observed for an individual. We find that, in general, this is difficult for quantiles other than the median unless strong assumptions are imposed on how yt is related to yc. For the median, we present conditions under which the sign of the median treatment effect is identified.

Suggested Citation

  • Myoung‐jae Lee, 2000. "Median treatment effect in randomized trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 595-604.
    • Handle: RePEc:bla:jorssb:v:62:y:2000:i:3:p:595-604
    DOI: 10.1111/1467-9868.00253
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    Citations

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    Cited by:

    1. Choi, Jin-young & Lee, Myoung-jae, 2019. "Twins are more different than commonly believed, but made less different by compensating behaviors," Economics & Human Biology, Elsevier, vol. 35(C), pages 18-31.
    2. Mullahy, John, 2018. "Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects," Journal of Health Economics, Elsevier, vol. 61(C), pages 151-162.
    3. John Mullahy, 2020. "Discovering Treatment Effectiveness via Median Treatment Effects—Applications to COVID-19 Clinical Trials," NBER Working Papers 27895, National Bureau of Economic Research, Inc.
    4. Fan, Yanqin & Park, Sang Soo, 2012. "Confidence intervals for the quantile of treatment effects in randomized experiments," Journal of Econometrics, Elsevier, vol. 167(2), pages 330-344.
    5. Myoung-Jae Lee, 2004. "Selection correction and sensitivity analysis for ordered treatment effect on count response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(3), pages 323-337.
    6. Liu, Zhen & Lan, Jing, 2015. "The Sloping Land Conversion Program in China: Effect on the Livelihood Diversification of Rural Households," World Development, Elsevier, vol. 70(C), pages 147-161.
    7. John Mullahy, 2017. "Individual Results May Vary: Elementary Analytics of Inequality-Probability Bounds, with Applications to Health-Outcome Treatment Effects," NBER Working Papers 23603, National Bureau of Economic Research, Inc.
    8. Myoung‐Jae Lee & Satoru Kobayashi, 2001. "Proportional treatment effects for count response panel data: effects of binary exercise on health care demand," Health Economics, John Wiley & Sons, Ltd., vol. 10(5), pages 411-428, July.
    9. Jin-young Choi & Myoung-jae Lee, 2017. "Regression discontinuity: review with extensions," Statistical Papers, Springer, vol. 58(4), pages 1217-1246, December.
    10. Myoung‐jae Lee & Sang‐jun Lee, 2005. "Analysis of job‐training effects on Korean women," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 549-562, May.
    11. Myoung-jae Lee, 2017. "Extensive and intensive margin effects in sample selection models: racial effects on wages," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 817-839, June.
    12. John Mullahy, 2021. "Discovering treatment effectiveness via median treatment effects—Applications to COVID‐19 clinical trials," Health Economics, John Wiley & Sons, Ltd., vol. 30(5), pages 1050-1069, May.
    13. Lee, Myoung-jae, 2012. "Treatment effects in sample selection models and their nonparametric estimation," Journal of Econometrics, Elsevier, vol. 167(2), pages 317-329.

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