Citations (31)
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Jacob Dorn & Kevin Guo. (2023) Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing. Journal of the American Statistical Association 118:544, pages 2645-2657.
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Iman Jaljuli, Yoav Benjamini, Liat Shenhav, Orestis A. Panagiotou & Ruth Heller. (2023) Quantifying Replicability and Consistency in Systematic Reviews. Statistics in Biopharmaceutical Research 15:2, pages 372-385.
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B. Zhang, D. S. Small, K. B. Lasater, M. McHugh, J. H. Silber & P. R. Rosenbaum. (2023) Matching One Sample According to Two Criteria in Observational Studies. Journal of the American Statistical Association 118:542, pages 1140-1151.
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Kwonsang Lee, Dylan S. Small & Francesca Dominici. (2021) Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies. Journal of the American Statistical Association 116:534, pages 569-580.
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Bikram Karmakar, Dylan S. Small & Paul R. Rosenbaum. (2021) Reinforced Designs: Multiple Instruments Plus Control Groups as Evidence Factors in an Observational Study of the Effectiveness of Catholic Schools. Journal of the American Statistical Association 116:533, pages 82-92.
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Bo Zhang, Jordan Weiss, Dylan S. Small & Qingyuan Zhao. (2021) Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding. Journal of the American Statistical Association 116:533, pages 295-308.
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Jianshen Chen & Bryan Keller. (2019) Heterogeneous Subgroup Identification in Observational Studies. Journal of Research on Educational Effectiveness 12:3, pages 578-596.
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Qingyuan Zhao. (2019) On Sensitivity Value of Pair-Matched Observational Studies. Journal of the American Statistical Association 114:526, pages 713-722.
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Paul R. Rosenbaum. (2017) Imposing Minimax and Quantile Constraints on Optimal Matching in Observational Studies. Journal of Computational and Graphical Statistics 26:1, pages 66-78.
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Paul R. Rosenbaum. (2015) Some Counterclaims Undermine Themselves in Observational Studies. Journal of the American Statistical Association 110:512, pages 1389-1398.
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Paul R. Rosenbaum. (2015) Bahadur Efficiency of Sensitivity Analyses in Observational Studies. Journal of the American Statistical Association 110:509, pages 205-217.
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Articles from other publishers (20)
Paul R. Rosenbaum. (2023) A Second Evidence Factor for a Second Control Group. Biometrics 79:4, pages 3968-3980.
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Ting Ye, Dylan S. Small & Paul R. Rosenbaum. (2022) Dimensions, power and factors in an observational study of behavioral problems after physical abuse of children. The Annals of Applied Statistics 16:4.
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Paul R. Rosenbaum. (2022)
A statistic with demonstrated insensitivity to unmeasured bias for 2 × 2 ×
S
tables in observational studies
. Statistics in Medicine 41:19, pages 3758-3771.
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Anqi Zhao, Youjin Lee, Dylan S. Small & Bikram Karmakar. (2022) Evidence factors from multiple, possibly invalid, instrumental variables. The Annals of Statistics 50:3.
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Michael Johnson, Jiongyi Cao & Hyunseung Kang. (2022) Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon health insurance experiment. The Annals of Applied Statistics 16:2.
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Yuyang Zhang, Patrick Schnell, Chi Song, Bin Huang & Bo Lu. (2021) Subgroup causal effect identification and estimation via matching tree. Computational Statistics & Data Analysis 159, pages 107188.
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Bikram Karmakar & Dylan S. Small. (2020) Assessment of the extent of corroboration of an elaborate theory of a causal hypothesis using partial conjunctions of evidence factors. The Annals of Statistics 48:6.
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P R Rosenbaum. (2020) A conditional test with demonstrated insensitivity to unmeasured bias in matched observational studies. Biometrika 107:4, pages 827-840.
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Bikram Karmakar, Chyke A. Doubeni & Dylan S. Small. (2020) Evidence factors in a case-control study with application to the effect of flexible sigmoidoscopy screening on colorectal cancer. The Annals of Applied Statistics 14:2.
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Bikram Karmakar, Dylan S Small & Paul R Rosenbaum. (2020) Using Evidence Factors to Clarify Exposure Biomarkers. American Journal of Epidemiology 189:3, pages 243-249.
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Paul R. RosenbaumPaul R. Rosenbaum. 2020. Design of Observational Studies. Design of Observational Studies
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B Karmakar, B French & D S Small. (2019) Integrating the evidence from evidence factors in observational studies. Biometrika 106:2, pages 353-367.
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Hao Jiang, Min Lin, Bingqing Liu, Huifang Liu, Yuanyuan Zeng, He Nai, Xiaoli Zhang, Xianlong Zhao, Wen Du & Haining Ye. 2019. Smart Computing and Communication. Smart Computing and Communication
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Kwonsang Lee, Dylan S. Small & Paul R. Rosenbaum. (2018) A Powerful Approach to the Study of Moderate Effect Modification in Observational Studies. Biometrics 74:4, pages 1161-1170.
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Bikram Karmakar, Ruth Heller & Dylan S. Small. (2018) False discovery rate control for effect modification in observational studies. Electronic Journal of Statistics 12:2.
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Paul R. Rosenbaum. (2017) The General Structure of Evidence Factors in Observational Studies. Statistical Science 32:4.
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Paul R. Rosenbaum & Dylan S. Small. (2017) An Adaptive Mantel–Haenszel Test for Sensitivity Analysis in Observational Studies. Biometrics 73:2, pages 422-430.
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Jesse Y. Hsu, José R. Zubizarreta, Dylan S. Small & Paul R. Rosenbaum. (2015) Strong control of the familywise error rate in observational studies that discover effect modification by exploratory methods. Biometrika 102:4, pages 767-782.
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Paul R. Rosenbaum. (2015) How to See More in Observational Studies: Some New Quasi-Experimental Devices. Annual Review of Statistics and Its Application 2:1, pages 21-48.
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