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A Distributional Approach for Causal Inference Using Propensity Scores

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Cited by:

  1. Anastasios A. Tsiatis & Marie Davidian & Weihua Cao, 2011. "Improved Doubly Robust Estimation When Data Are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout," Biometrics, The International Biometric Society, vol. 67(2), pages 536-545, June.
  2. Essilfie, Felix Larry, 2018. "Varietal seed technology and household income of maize farmers: An application of the doubly robust model," Technology in Society, Elsevier, vol. 55(C), pages 85-91.
  3. Evan T.R. Rosenman & Guillaume Basse & Art B. Owen & Mike Baiocchi, 2023. "Combining observational and experimental datasets using shrinkage estimators," Biometrics, The International Biometric Society, vol. 79(4), pages 2961-2973, December.
  4. Jing Qin & Biao Zhang, 2007. "Empirical‐likelihood‐based inference in missing response problems and its application in observational studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 101-122, February.
  5. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
  6. Słoczyński, Tymon & Wooldridge, Jeffrey M., 2018. "A General Double Robustness Result For Estimating Average Treatment Effects," Econometric Theory, Cambridge University Press, vol. 34(1), pages 112-133, February.
  7. Jacob Dorn & Kevin Guo & Nathan Kallus, 2021. "Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding," Papers 2112.11449, arXiv.org, revised Jul 2022.
  8. Ashesh Rambachan & Amanda Coston & Edward Kennedy, 2022. "Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding," Papers 2212.09844, arXiv.org, revised May 2024.
  9. Dan Yang & Dylan S. Small & Jeffrey H. Silber & Paul R. Rosenbaum, 2012. "Optimal Matching with Minimal Deviation from Fine Balance in a Study of Obesity and Surgical Outcomes," Biometrics, The International Biometric Society, vol. 68(2), pages 628-636, June.
  10. 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.
  11. Sun Hao & Ertefaie Ashkan & Lu Xin & Johnson Brent A., 2020. "Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 300-314, January.
  12. 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.
  13. Yu-Jen Cheng & Mei-Cheng Wang, 2015. "Causal estimation using semiparametric transformation models under prevalent sampling," Biometrics, The International Biometric Society, vol. 71(2), pages 302-312, June.
  14. Yu-Jen Cheng & Mei-Cheng Wang, 2012. "Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 707-716, September.
  15. Han, Peisong & Song, Peter X.-K. & Wang, Lu, 2015. "Achieving semiparametric efficiency bound in longitudinal data analysis with dropouts," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 59-70.
  16. Y Cui & E J Tchetgen Tchetgen, 2024. "Selective machine learning of doubly robust functionals," Biometrika, Biometrika Trust, vol. 111(2), pages 517-535.
  17. Tan Zhiqiang, 2008. "Comment: Improved Local Efficiency and Double Robustness," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-11, June.
  18. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
  19. repec:bla:istatr:v:83:y:2015:i:3:p:449-471 is not listed on IDEAS
  20. Sylvie Goetgeluk & Stijn Vansteelandt & Els Goetghebeur, 2008. "Estimation of controlled direct effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 1049-1066, November.
  21. Michal Juraska & Peter B. Gilbert, 2016. "Mark-specific hazard ratio model with missing multivariate marks," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(4), pages 606-625, October.
  22. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
  23. Stijn Vansteelandt, 2012. "Discussions," Biometrics, The International Biometric Society, vol. 68(3), pages 675-678, September.
  24. Lan Wen & Miguel A. Hernán & James M. Robins, 2022. "Multiply robust estimators of causal effects for survival outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1304-1328, September.
  25. 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.
  26. Han, Peisong, 2012. "A note on improving the efficiency of inverse probability weighted estimator using the augmentation term," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2221-2228.
  27. Guo, Donglin & Xue, Liugen & Hu, Yuqin, 2017. "Covariate-balancing-propensity-score-based inference for linear models with missing responses," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 139-145.
  28. Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.
  29. Uysal, S. Derya, 2013. "Doubly Robust Estimation of Causal Effects with Multivalued Treatments," Economics Series 297, Institute for Advanced Studies.
  30. Tan, Zhiqiang, 2014. "Second-order asymptotic theory for calibration estimators in sampling and missing-data problems," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 240-253.
  31. Weiwei Wang & Daniel Scharfstein & Zhiqiang Tan & Ellen J. MacKenzie, 2009. "Causal inference in outcome‐dependent two‐phase sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 947-969, November.
  32. Lefebvre Geneviève & Gustafson Paul, 2010. "Impact of Outcome Model Misspecification on Regression and Doubly-Robust Inverse Probability Weighting to Estimate Causal Effect," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-27, March.
  33. Nathan Kallus, 2022. "What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment," Papers 2205.10327, arXiv.org, revised Nov 2022.
  34. 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.
  35. McCandless Lawrence C & Douglas Ian J. & Evans Stephen J. & Smeeth Liam, 2010. "Cutting Feedback in Bayesian Regression Adjustment for the Propensity Score," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-24, March.
  36. Helene Boistard & Guillaume Chauvet & David Haziza, 2016. "Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 683-699, September.
  37. 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.
  38. 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.
  39. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Papers 2206.08503, arXiv.org, revised Apr 2024.
  40. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
  41. 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.
  42. Xinkun Nie & Guido Imbens & Stefan Wager, 2021. "Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations," Papers 2112.04723, arXiv.org.
  43. Chen, Sixia & Haziza, David, 2018. "Jackknife empirical likelihood method for multiply robust estimation with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 258-268.
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