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The prognostic analogue of the propensity score

Citations

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Cited by:

  1. Zhexiao Lin & Peng Ding & Fang Han, 2023. "Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect," Econometrica, Econometric Society, vol. 91(6), pages 2187-2217, November.
  2. Shu Yang & Yunshu Zhang, 2023. "Multiply robust matching estimators of average and quantile treatment effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 235-265, March.
  3. 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.
  4. Roland R. Ramsahai, 2018. "Defining and estimating stochastic rate change in a dynamic general insurance portfolio," Papers 1810.10970, arXiv.org.
  5. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
  6. George Planiteros, 2022. "Reverse matching for ex-ante policy evaluation," DEOS Working Papers 2206, Athens University of Economics and Business.
  7. Chenyin Gao & Katherine Jenny Thompson & Jae Kwang Kim & Shu Yang, 2022. "Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1903-1930, October.
  8. Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.
  9. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2021. "The Augmented Synthetic Control Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1789-1803, October.
  10. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2021. "Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison," Stats, MDPI, vol. 4(2), pages 1-21, June.
  11. Corwin Matthew Zigler & Francesca Dominici, 2014. "Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model-Averaged Causal Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 95-107, March.
  12. de Luna, Xavier & Johansson, Per & Sjöstedt-de Luna, Sara, 2010. "Bootstrap inference for K-nearest neighbour matching estimators," Working Paper Series 2010:13, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  13. Pengzhou Wu & Kenji Fukumizu, 2021. "Towards Principled Causal Effect Estimation by Deep Identifiable Models," Papers 2109.15062, arXiv.org, revised Nov 2021.
  14. E. I. George & V. Ročková & P. R. Rosenbaum & V. A. Satopää & J. H. Silber, 2017. "Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 933-947, July.
  15. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
  16. Adam C. Sales & Ben B. Hansen & Brian Rowan, 2018. "Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates," Journal of Educational and Behavioral Statistics, , vol. 43(1), pages 3-31, February.
  17. José R. Zubizarreta, 2012. "Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1360-1371, December.
  18. Lee, Myoung-jae & Lee, Sanghyeok, 2019. "Double robustness without weighting," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 175-180.
  19. Kevin He & Yun Li & Panduranga S. Rao & Randall S. Sung & Douglas E. Schaubel, 2020. "Prognostic score matching methods for estimating the average effect of a non-reversible binary time-dependent treatment on the survival function," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 451-470, July.
  20. Thomas C. Buchmueller & John DiNardo & Robert G. Valletta, 2011. "The Effect of an Employer Health Insurance Mandate on Health Insurance Coverage and the Demand for Labor: Evidence from Hawaii," American Economic Journal: Economic Policy, American Economic Association, vol. 3(4), pages 25-51, November.
  21. David Cheng & Abhishek Chakrabortty & Ashwin N. Ananthakrishnan & Tianxi Cai, 2020. "Estimating average treatment effects with a double‐index propensity score," Biometrics, The International Biometric Society, vol. 76(3), pages 767-777, September.
  22. Aloyce R. Kaliba & Anne G. Gongwe & Kizito Mazvimavi & Ashagre Yigletu, 2021. "Impact of Adopting Improved Seeds on Access to Broader Food Groups Among Small-Scale Sorghum Producers in Tanzania," SAGE Open, , vol. 11(1), pages 21582440209, January.
  23. Jenny Häggström & Xavier Luna, 2014. "Targeted smoothing parameter selection for estimating average causal effects," Computational Statistics, Springer, vol. 29(6), pages 1727-1748, December.
  24. Ming-Yueh Huang & Kwun Chuen Gary Chan, 2017. "Joint sufficient dimension reduction and estimation of conditional and average treatment effects," Biometrika, Biometrika Trust, vol. 104(3), pages 583-596.
  25. Harsh Parikh & Cynthia Rudin & Alexander Volfovsky, 2018. "MALTS: Matching After Learning to Stretch," Papers 1811.07415, arXiv.org, revised Jun 2023.
  26. Susan Athey & Raj Chetty & Guido Imbens & Hyunseung Kang, 2016. "Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index," Papers 1603.09326, arXiv.org, revised Aug 2024.
  27. Samuel D. Pimentel & Lauren Vollmer Forrow & Jonathan Gellar & Jiaqi Li, 2020. "Optimal matching approaches in health policy evaluations under rolling enrolment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1411-1435, October.
  28. Cory Koedel & Diyi Li & Morgan S. Polikoff & Tenice Hardaway & Stephani L. Wrabel, 2016. "Mathematics Curriculum Effects on Student Achievement in California," Working Papers 1612, Department of Economics, University of Missouri.
  29. Jin-young Choi & Myoung-jae Lee, 2017. "Regression discontinuity: review with extensions," Statistical Papers, Springer, vol. 58(4), pages 1217-1246, December.
  30. D’Amour, Alexander & Ding, Peng & Feller, Avi & Lei, Lihua & Sekhon, Jasjeet, 2021. "Overlap in observational studies with high-dimensional covariates," Journal of Econometrics, Elsevier, vol. 221(2), pages 644-654.
  31. Zonghui Hu & Dean A. Follmann & Jing Qin, 2012. "Semiparametric Double Balancing Score Estimation for Incomplete Data With Ignorable Missingness," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 247-257, March.
  32. Clara Bicalho & Adam Bouyamourn & Thad Dunning, 2022. "Conditional Balance Tests: Increasing Sensitivity and Specificity With Prognostic Covariates," Papers 2205.10478, arXiv.org.
  33. Harsh Parikh, 2022. "Are Synthetic Control Weights Balancing Score?," Papers 2211.01575, arXiv.org.
  34. Richard Aviles-Lopez & Juan de Dios Luna del Castillo & Miguel Ángel Montero-Alonso, 2023. "Exploratory Matching Model Search Algorithm (EMMSA) for Causal Analysis: Application to the Cardboard Industry," Mathematics, MDPI, vol. 11(21), pages 1-34, October.
  35. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.
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