IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp8084.html
   My bibliography  Save this paper

A General Double Robustness Result for Estimating Average Treatment Effects

Author

Listed:
  • Sloczynski, Tymon

    (Brandeis University)

  • Wooldridge, Jeffrey M.

    (Michigan State University)

Abstract

In this paper we study doubly robust estimators of various average treatment effects under unconfoundedness. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for subpopulation-specific average treatment effects where subpopulations can be based on covariate values in an arbitrary way. Similar to Wooldridge (2007), we then discuss estimation of the conditional mean using quasi-log likelihoods (QLL) from the linear exponential family.

Suggested Citation

  • Sloczynski, Tymon & Wooldridge, Jeffrey M., 2014. "A General Double Robustness Result for Estimating Average Treatment Effects," IZA Discussion Papers 8084, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp8084
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp8084.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cattaneo, Matias D., 2010. "Efficient semiparametric estimation of multi-valued treatment effects under ignorability," Journal of Econometrics, Elsevier, vol. 155(2), pages 138-154, April.
    2. Weihua Cao & Anastasios A. Tsiatis & Marie Davidian, 2009. "Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data," Biometrika, Biometrika Trust, vol. 96(3), pages 723-734.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588.
    4. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.
    5. Rothe, Christoph & Firpo, Sergio Pinheiro, 2013. "Semiparametric estimation and inference using doubly robust moment conditions," Textos para discussão 330, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Patrick Kline, 2011. "Oaxaca-Blinder as a Reweighting Estimator," American Economic Review, American Economic Association, vol. 101(3), pages 532-537, May.
    7. Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
    8. Tan, Zhiqiang, 2006. "A Distributional Approach for Causal Inference Using Propensity Scores," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1619-1637, December.
    9. Uysal, S. Derya, 2013. "Doubly Robust Estimation of Causal Effects with Multivalued Treatments," Economics Series 297, Institute for Advanced Studies.
    10. Boris Kaiser, 2016. "Decomposing differences in arithmetic means: a doubly robust estimation approach," Empirical Economics, Springer, vol. 50(3), pages 873-899, May.
    11. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    12. Andrea Rotnitzky & Quanhong Lei & Mariela Sued & James M. Robins, 2012. "Improved double-robust estimation in missing data and causal inference models," Biometrika, Biometrika Trust, vol. 99(2), pages 439-456.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    2. Hartley, Robert Paul & Lamarche, Carlos, 2018. "Behavioral responses and welfare reform: Evidence from a randomized experiment," Labour Economics, Elsevier, vol. 54(C), pages 135-151.
    3. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    4. Arthur Lewbel & Jin-Young Choi & Zhuzhu Zhou, 2019. "Over-Identified Doubly Robust Identification and Estimation," Boston College Working Papers in Economics 1003, Boston College Department of Economics, revised 01 Feb 2021.
    5. Jörg Kalbfuß & Reto Odermatt & Alois Stutzer, 2018. "Medical marijuana laws and mental health in the United States," CEP Discussion Papers dp1546, Centre for Economic Performance, LSE.
    6. Zetterqvist, Johan & Waernbaum, Ingeborg, 2020. "Semi-parametric estimation of multi-valued treatment effects for the treated:estimating equations and sandwich estimators," Working Paper Series 2020:4, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    7. Levchak, Philip J., 2021. "Stop-and-frisk in New York City: Estimating racial disparities in post-stop outcomes," Journal of Criminal Justice, Elsevier, vol. 73(C).
    8. Nikolic, Jelena & Rubil, Ivica & Tomić, Iva, 2017. "Pre-crisis reforms, austerity measures and the public-private wage gap in two emerging economies," Economic Systems, Elsevier, vol. 41(2), pages 248-265.
    9. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    10. Brantly Callaway & Tong Li, 2021. "Policy Evaluation during a Pandemic," Papers 2105.06927, arXiv.org.
    11. George Mgendi & Shiping Mao & Fangbin Qiao, 2021. "Is a Training Program Sufficient to Improve the Smallholder Farmers’ Productivity in Africa? Empirical Evidence from a Chinese Agricultural Technology Demonstration Center in Tanzania," Sustainability, MDPI, vol. 13(3), pages 1-23, February.
    12. Long, Wenjin & Pang, Xiaopeng & Dong, Xiao-yuan & Zeng, Junxia, 2020. "Is rented accommodation a good choice for primary school students' academic performance? – Evidence from rural China," China Economic Review, Elsevier, vol. 62(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    2. Jianxuan Liu & Yanyuan Ma & Lan Wang, 2018. "An alternative robust estimator of average treatment effect in causal inference," Biometrics, The International Biometric Society, vol. 74(3), pages 910-923, September.
    3. Garbero, Alessandra & Songsermsawas, Tisorn, 2016. "Impact of modern irrigation on household production and welfare outcomes: Evidence from the PASIDP project in Ethiopia," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235949, Agricultural and Applied Economics Association.
    4. Hao Cheng & Ying Wei, 2018. "A fast imputation algorithm in quantile regression," Computational Statistics, Springer, vol. 33(4), pages 1589-1603, December.
    5. Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
    6. Bryan S. Graham & Cristine Campos de Xavier Pinto, 2018. "Semiparametrically efficient estimation of the average linear regression function," Papers 1810.12511, arXiv.org.
    7. Chris Muris, 2020. "Efficient GMM Estimation with Incomplete Data," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 518-530, July.
    8. Karel Vermeulen & Stijn Vansteelandt, 2015. "Bias-Reduced Doubly Robust Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1024-1036, September.
    9. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2016. "Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 288-301, April.
    10. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    11. Peisong Han, 2014. "Multiply Robust Estimation in Regression Analysis With Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1159-1173, September.
    12. Garbero, A. & Songsermsawas, T., 2018. "IFAD RESEARCH SERIES 31 - Impact of modern irrigation on household production and welfare outcomes: evidence from the participatory small-scale irrigation development programme (PASIDP) project in Eth," IFAD Research Series 280080, International Fund for Agricultural Development (IFAD).
    13. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    14. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP57/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Zheng, Yanqiao & Zhang, Xiaoqi & Zhu, Yu, 2021. "Overeducation, major mismatch, and return to higher education tiers: Evidence from novel data source of a major online recruitment platform in China," China Economic Review, Elsevier, vol. 66(C).
    16. Yukichi Mano & Kazushi Takahashi & Keijiro Otsuka, 2020. "Mechanization in land preparation and agricultural intensification: The case of rice farming in the Cote d'Ivoire," Agricultural Economics, International Association of Agricultural Economists, vol. 51(6), pages 899-908, November.
    17. 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.
    18. Xiaogang Duan & Guosheng Yin, 2017. "Ensemble Approaches to Estimating the Population Mean with Missing Response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 899-917, December.
    19. Zhiwei Zhang & Zhen Chen & James F. Troendle & Jun Zhang, 2012. "Causal Inference on Quantiles with an Obstetric Application," Biometrics, The International Biometric Society, vol. 68(3), pages 697-706, September.
    20. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).

    More about this item

    Keywords

    double robustness; inverse-probability weighting (IPW); multi-valued treatments; quasi-maximum likelihood estimation (QMLE); treatment effects;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp8084. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.