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Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data

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  • Li, Qi
  • Racine, Jeffrey S.
  • Wooldridge, Jeffrey M.

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  • Li, Qi & Racine, Jeffrey S. & Wooldridge, Jeffrey M., 2009. "Efficient Estimation of Average Treatment Effects with Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 206-223.
    • Handle: RePEc:bes:jnlbes:v:27:i:2:y:2009:p:206-223
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    Cited by:

    1. Persson, Emma & Häggström, Jenny & Waernbaum, Ingeborg & de Luna, Xavier, 2017. "Data-driven algorithms for dimension reduction in causal inference," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 280-292.
    2. Lv, Xiaofeng & Li, Rui & Fang, Zheng, 2017. "Efficient semiparametric estimation for Gini inequality treatment effects," Economics Letters, Elsevier, vol. 154(C), pages 96-100.
    3. Tang, Shengfang & Huang, Zhilin, 2022. "Empirical likelihood confidence interval for difference-in-differences estimator with panel data," Economics Letters, Elsevier, vol. 216(C).
    4. Kyoo il Kim, 2019. "Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric," Econometrics, MDPI, vol. 7(2), pages 1-13, May.
    5. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    6. Valentin Zelenyuk & Leopold Simar, 2011. "To Smooth or Not to Smooth? The Case of Discrete Variables in Nonparametric Regressions," CEPA Working Papers Series WP102011, School of Economics, University of Queensland, Australia.
    7. Daniel Wikström, 2015. "A finite sample improvement of the fixed effects estimator applied to technical inefficiency," Journal of Productivity Analysis, Springer, vol. 43(1), pages 29-46, February.
    8. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    9. Bhattacharya, Jay & Shaikh, Azeem M. & Vytlacil, Edward, 2012. "Treatment effect bounds: An application to Swan–Ganz catheterization," Journal of Econometrics, Elsevier, vol. 168(2), pages 223-243.
    10. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    11. Vera Chiodi & Gabriel Montes‐Rojas, 2022. "Mentoring as a dose treatment: Frequency matters—Evidence from a French mentoring programme," LABOUR, CEIS, vol. 36(2), pages 145-166, June.
    12. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    13. Francesco Bravo & David Jacho-Chavez, 2011. "Empirical Likelihood for Efficient Semiparametric Average Treatment Effects," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 1-24.
    14. Dehejia Rajeev, 2015. "Experimental and Non-Experimental Methods in Development Economics: A Porous Dialectic," Journal of Globalization and Development, De Gruyter, vol. 6(1), pages 47-69, June.
    15. White, Halbert, 2006. "Time-series estimation of the effects of natural experiments," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 527-566.
    16. Huber, Martin, 2012. "Identifying causal mechanisms in experiments (primarily) based on inverse probability weighting," Economics Working Paper Series 1213, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    17. Giovanni Cerulli, 2013. "treatrew: A user-written Stata routine for estimating average treatment effects by reweighting on propensity score," United Kingdom Stata Users' Group Meetings 2013 02, Stata Users Group.
    18. Wikstrom, Daniel & Peeters, Ludo & Surry, Yves R., 2011. "Semiparametric Cost Allocation Estimation," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115742, European Association of Agricultural Economists.
    19. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    20. Kim P. Huynh & David T. Jacho-Chávez & James K. Self, 2015. "The Distributional Efficacy of Collaborative Learning on Student Outcomes," The American Economist, Sage Publications, vol. 60(2), pages 98-119, September.

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