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Regression and Weighting Methods for Causal Inference Using Instrumental Variables

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  • Tan, Zhiqiang

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  • Tan, Zhiqiang, 2006. "Regression and Weighting Methods for Causal Inference Using Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1607-1618, December.
  • Handle: RePEc:bes:jnlasa:v:101:y:2006:p:1607-1618
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    1. repec:wly:japmet:v:32:y:2017:i:7:p:1207-1225 is not listed on IDEAS
    2. Shinohara Russell T. & Frangakis Constantine E. & Platz Elizabeth & Tsilidis Konstantinos, 2012. "Designs Combining Instrumental Variables with Case-Control: Estimating Principal Strata Causal Effects," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-21, January.
    3. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    4. Fricke, Hans & Frölich, Markus & Huber, Martin & Lechner, Michael, 2015. "Endogeneity and non-response bias in treatment evaluation - nonparametric identification of causal effects by instruments," FSES Working Papers 459, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    5. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    6. repec:bla:jorssa:v:180:y:2017:i:2:p:569-586 is not listed on IDEAS
    7. 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, Getulio Vargas Foundation (Brazil).
    8. repec:bla:jorssb:v:79:y:2017:i:5:p:1645-1666 is not listed on IDEAS
    9. Paul S. Clarke & Frank Windmeijer, 2012. "Instrumental Variable Estimators for Binary Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1638-1652, December.
    10. Foster, E. Michael & McCombs-Thornton, Kimberly, 2013. "Child welfare and the challenge of causal inference," Children and Youth Services Review, Elsevier, vol. 35(7), pages 1130-1142.
    11. James Robins & Andrea Rotnitzky & Stijn Vansteelandt, 2007. "Discussions," Biometrics, The International Biometric Society, vol. 63(3), pages 650-653, September.
    12. Chunrong Ai & Lukang Huang & Zheng Zhang, 2018. "A Simple and Efficient Estimation of the Average Treatment Effect in the Presence of Unmeasured Confounders," Papers 1807.05678, arXiv.org.
    13. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    14. Pohlmeier, Winfried & Seiberlich, Ruben & Uysal, Selver Derya, 2016. "A simple and successful shrinkage method for weighting estimators of treatment effects," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 512-525.
    15. 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.
    16. Uysal, S. Derya, 2013. "Doubly Robust Estimation of Causal Effects with Multivalued Treatments," Economics Series 297, Institute for Advanced Studies.
    17. Fan Yang & José R. Zubizarreta & Dylan S. Small & Scott Lorch & Paul R. Rosenbaum, 2014. "Dissonant Conclusions When Testing the Validity of an Instrumental Variable," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 253-263, November.

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