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Doubly robust estimation of the local average treatment effect curve

Citations

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Cited by:

  1. Yumou Qiu & Jing Tao & Xiao‐Hua Zhou, 2021. "Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 1016-1043, November.
  2. Wang Yiming & Liu Yi & Yang Shu, 2025. "Rate doubly robust estimation for weighted average treatment effects," Journal of Causal Inference, De Gruyter, vol. 13(1), pages 1-28.
  3. Zhichao Jiang & Shu Yang & Peng Ding, 2022. "Multiply robust estimation of causal effects under principal ignorability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1423-1445, September.
  4. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
  5. 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.
  6. Wei, Bo & Tan, Kean Ming & He, Xuming, 2024. "Estimation of complier expected shortfall treatment effects with a binary instrumental variable," Journal of Econometrics, Elsevier, vol. 238(2).
  7. Tymon Słoczyński & S. Derya Uysal & Jeffrey M. Wooldridge, 2025. "Abadie’s Kappa and Weighting Estimators of the Local Average Treatment Effect," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 164-177, January.
  8. Maria Cuellar & Edward H. Kennedy, 2020. "A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1793-1818, October.
  9. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
  10. Choi, Jin-young & Lee, Goeun & Lee, Myoung-jae, 2023. "Endogenous treatment effect for any response conditional on control propensity score," Statistics & Probability Letters, Elsevier, vol. 196(C).
  11. Kara E. Rudolph & Iván Díaz, 2022. "When the ends do not justify the means: Learning who is predicted to have harmful indirect effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 573-589, December.
  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. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
  14. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Testing Unconfoundedness Assumption Using Auxiliary Variables," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201905, University of Kansas, Department of Economics, revised Mar 2019.
  15. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.
  16. Pan Zhao & Yifan Cui, 2023. "A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning," Papers 2310.09545, arXiv.org.
  17. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2020. "Testing Unconfoundedness Assumption Using Auxiliary Variables," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202004, University of Kansas, Department of Economics, revised Feb 2020.
  18. Myoung‐jae Lee, 2021. "Instrument residual estimator for any response variable with endogenous binary treatment," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 612-635, July.
  19. Ting Ye & Ashkan Ertefaie & James Flory & Sean Hennessy & Dylan S. Small, 2023. "Instrumented difference‐in‐differences," Biometrics, The International Biometric Society, vol. 79(2), pages 569-581, June.
  20. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
  21. Jierui Du & Gao Wen & Xin Liang, 2024. "Estimating the Complier Average Causal Effect with Non-Ignorable Missing Outcomes Using Likelihood Analysis," Mathematics, MDPI, vol. 12(9), pages 1-16, April.
  22. Chimwemwe Misomali & Gowokani Chijere Chirwa, 2025. "The Impact of Financial Inclusion on Multidimensional Poverty among Females In Malawi," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 180(3), pages 1853-1873, December.
  23. Yu, Haiyan & Yang, Ching-Chi & Yu, Ping, 2023. "Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1355-1364.
  24. Andreas Nordland & Torben Martinussen, 2024. "Estimation of treatment effect among treatment responders with a time‐to‐event endpoint," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(3), pages 1161-1180, September.
  25. Rahul Singh & Liyang Sun, 2024. "Double robustness for complier parameters and a semi-parametric test for complier characteristics," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 1-20.
  26. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP77/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  27. Tymon Sloczynski & S. Derya Uysal & Jeffrey M. Wooldridge & Tymon Słoczyński & Derya Uysal, 2022. "Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment," CESifo Working Paper Series 10105, CESifo.
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