Abadie, Alberto, and Guido W. Imbens. 2006. “Large SampleProperties of Matching Estimators for Average Treatment Effects.”Econometrica 74 (1): 235–67. https://doi.org/10.1111/j.1468-0262.2006.00655.x.
———. 2016. “Matching on the Estimated Propensity Score.”Econometrica 84 (2): 781–807. https://doi.org/10.3982/ECTA11293.
Austin, Peter C. 2009. “The Relative Ability of DifferentPropensity Score Methods to Balance Measured Covariates Between Treatedand Untreated Subjects in Observational Studies.” MedicalDecision Making 29 (6): 661–77. https://doi.org/10.1177/0272989x09341755.
———. 2010a. “The Performance of Different Propensity-Score Methodsfor Estimating Differences in Proportions (Risk Differences or AbsoluteRisk Reductions) in Observational Studies.” Statistics inMedicine 29 (20): 2137–48. https://doi.org/10.1002/sim.3854.
———. 2010b. “Statistical Criteria for Selecting the Optimal Numberof Untreated Subjects Matched to Each Treated Subject When UsingMany-to-One Matching on the Propensity Score.” AmericanJournal of Epidemiology 172 (9): 1092–97. https://doi.org/10.1093/aje/kwq224.
———. 2011. “Optimal Caliper Widths forPropensity-Score Matching When Estimating Differences inMeans and Differences in Proportions in Observational Studies.”Pharmaceutical Statistics 10 (2): 150–61. https://doi.org/10.1002/pst.433.
———. 2013. “A Comparison of 12 Algorithms for Matching on thePropensity Score.” Statistics in Medicine 33 (6):1057–69. https://doi.org/10.1002/sim.6004.
Austin, Peter C., and Guy Cafri. 2020. “Variance Estimation WhenUsing Propensity-Score Matching with Replacement withSurvival or Time-to-Event Outcomes.”Statistics in Medicine 39 (11): 1623–40. https://doi.org/10.1002/sim.8502.
Austin, Peter C., and Dylan S. Small. 2014. “The Use ofBootstrapping When Using Propensity-Score Matching WithoutReplacement: A Simulation Study.” Statistics in Medicine33 (24): 4306–19. https://doi.org/10.1002/sim.6276.
Austin, Peter C., and Elizabeth A. Stuart. 2015a. “The Performanceof Inverse Probability of Treatment Weighting and Full Matching on thePropensity Score in the Presence of Model Misspecification WhenEstimating the Effect of Treatment on Survival Outcomes.”Statistical Methods in Medical Research 26 (4): 1654–70. https://doi.org/10.1177/0962280215584401.
———. 2015b. “Estimating the Effect of Treatment on Binary OutcomesUsing Full Matching on the Propensity Score.” StatisticalMethods in Medical Research 26 (6): 2505–25. https://doi.org/10.1177/0962280215601134.
Cohn, Eric R., and Jose R. Zubizarreta. 2021. “ProfileMatching for the Generalization andPersonalization of Causal Inferences.”arXiv:2105.10060 [Stat], May. https://arxiv.org/abs/2105.10060.
de los Angeles Resa, María, and José R. Zubizarreta. 2020. “Directand Stable Weight Adjustment in Non-Experimental Studies withMultivalued Treatments: Analysis of the Effect of an Earthquake onPost-Traumatic Stress.” Journal of the Royal StatisticalSociety: Series A (Statistics in Society) n/a (n/a). https://doi.org/10.1111/rssa.12561.
Desai, Rishi J., Kenneth J. Rothman, Brian T. Bateman, SoniaHernandez-Diaz, and Krista F. Huybrechts. 2017. “APropensity-Score-Based Fine Stratification Approach for ConfoundingAdjustment When Exposure Is Infrequent:” Epidemiology 28(2): 249–57. https://doi.org/10.1097/EDE.0000000000000595.
Diamond, Alexis, and Jasjeet S. Sekhon. 2013. “Genetic Matchingfor Estimating Causal Effects: A General Multivariate Matching Methodfor Achieving Balance in Observational Studies.” Review ofEconomics and Statistics 95 (3): 932945. https://doi.org/10.1162/REST_a_00318.
Gu, Xing Sam, and Paul R. Rosenbaum. 1993. “Comparison ofMultivariate Matching Methods: Structures, Distances, andAlgorithms.” Journal of Computational and GraphicalStatistics 2 (4): 405. https://doi.org/10.2307/1390693.
Hansen, Ben B. 2004. “Full Matching in an Observational Study ofCoaching for the SAT.” Journal of the American StatisticalAssociation 99 (467): 609–18. https://doi.org/10.1198/016214504000000647.
———. 2008. “The Prognostic Analogue of the PropensityScore.” Biometrika 95 (2): 481–88. https://doi.org/10.1093/biomet/asn004.
Hansen, Ben B., and Stephanie O. Klopfer. 2006. “Optimal FullMatching and Related Designs via Network Flows.” Journal ofComputational and Graphical Statistics 15 (3): 609–27. https://doi.org/10.1198/106186006X137047.
Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. 2007.“Matching as Nonparametric Preprocessing for Reducing ModelDependence in Parametric Causal Inference.” PoliticalAnalysis 15 (3): 199–236. https://doi.org/10.1093/pan/mpl013.
Hong, Guanglei. 2010. “Marginal Mean Weighting ThroughStratification: Adjustment for Selection Bias in MultilevelData.” Journal of Educational and Behavioral Statistics35 (5): 499–531. https://doi.org/10.3102/1076998609359785.
Iacus, Stefano M., Gary King, and Giuseppe Porro. 2012. “CausalInference Without Balance Checking: Coarsened Exact Matching.”Political Analysis 20 (1): 1–24. https://doi.org/10.1093/pan/mpr013.
King, Gary, and Richard Nielsen. 2019. “Why Propensity ScoresShould Not Be Used for Matching.” Political Analysis,May, 1–20. https://doi.org/10.1017/pan.2019.11.
Mao, Huzhang, Liang Li, and Tom Greene. 2018. “Propensity ScoreWeighting Analysis and Treatment Effect Discovery.”Statistical Methods in Medical Research, June, 096228021878117.https://doi.org/10.1177/0962280218781171.
Ming, Kewei, and Paul R. Rosenbaum. 2000. “Substantial Gains inBias Reduction from Matching with a Variable Number of Controls.”Biometrics 56 (1): 118–24. https://doi.org/10.1111/j.0006-341X.2000.00118.x.
Orihara, Shunichiro, and Etsuo Hamada. 2021. “Determination of theOptimal Number of Strata for Propensity Score Subclassification.”Statistics & Probability Letters 168 (January): 108951. https://doi.org/10.1016/j.spl.2020.108951.
Rassen, Jeremy A., Abhi A. Shelat, Jessica Myers, Robert J. Glynn,Kenneth J. Rothman, and Sebastian Schneeweiss. 2012. “One-to-ManyPropensity Score Matching in Cohort Studies.”Pharmacoepidemiology and Drug Safety 21 (S2): 69–80. https://doi.org/10.1002/pds.3263.
Ripollone, John E., Krista F. Huybrechts, Kenneth J. Rothman, Ryan E.Ferguson, and Jessica M. Franklin. 2018. “Implications of thePropensity Score Matching Paradox inPharmacoepidemiology.” American Journal ofEpidemiology 187 (9): 1951–61. https://doi.org/10.1093/aje/kwy078.
Rosenbaum, Paul R. 2010. Design of Observational Studies.Springer Series in Statistics. New York:Springer.
———. 2020. “Modern Algorithms for Matching in ObservationalStudies.” Annual Review of Statistics and ItsApplication 7 (1): 143–76. https://doi.org/10.1146/annurev-statistics-031219-041058.
Rosenbaum, Paul R., and Donald B. Rubin. 1985a. “The Bias Due toIncomplete Matching.” Biometrics 41 (1): 103–16. https://doi.org/10.2307/2530647.
———. 1985b. “Constructing a Control Group Using MultivariateMatched Sampling Methods That Incorporate the Propensity Score.”The American Statistician 39 (1): 33. https://doi.org/10.2307/2683903.
Rubin, Donald B. 1973. “Matching to Remove Bias in ObservationalStudies.” Biometrics 29 (1): 159. https://doi.org/10.2307/2529684.
———. 1980. “Bias Reduction Using Mahalanobis-MetricMatching.” Biometrics 36 (2): 293–98. https://doi.org/10.2307/2529981.
Savje, Fredrik, Jasjeet Sekhon, and Michael Higgins. 2018.Quickmatch: Quick Generalized Full Matching. https://CRAN.R-project.org/package=quickmatch.
Sävje, Fredrik, Michael J. Higgins, and Jasjeet S. Sekhon. 2021.“Generalized Full Matching.” Political Analysis 29(4): 423–47. https://doi.org/10.1017/pan.2020.32.
Schafer, Joseph L., and Joseph Kang. 2008. “Average Causal Effectsfrom Nonrandomized Studies: A Practical Guide and SimulatedExample.” Psychological Methods 13 (4): 279–313. https://doi.org/10.1037/a0014268.
Sekhon, Jasjeet S. 2011. “Multivariate and Propensity ScoreMatching Software with Automated Balance Optimization: The MatchingPackage for R.” Journal of Statistical Software 42 (1):1–52. https://doi.org/10.18637/jss.v042.i07.
Stuart, Elizabeth A. 2008. “Developing Practical Recommendationsfor the Use of Propensity Scores: Discussion of ‘A CriticalAppraisal of Propensity Score Matching in the Medical Literature Between1996 and 2003’ by Peter Austin,Statistics inMedicine.” Statistics in Medicine 27 (12): 2062–65. https://doi.org/10.1002/sim.3207.
———. 2010. “Matching Methods for Causal Inference: A Review and aLook Forward.” Statistical Science 25 (1): 1–21. https://doi.org/10.1214/09-STS313.
Stuart, Elizabeth A., and Kerry M. Green. 2008. “Using FullMatching to Estimate Causal Effects in Nonexperimental Studies:Examining the Relationship Between Adolescent Marijuana Use and AdultOutcomes.” Developmental Psychology 44 (2): 395–406. https://doi.org/10.1037/0012-1649.44.2.395.
Thoemmes, Felix J., and Eun Sook Kim. 2011. “A Systematic Reviewof Propensity Score Methods in the Social Sciences.”Multivariate Behavioral Research 46 (1): 90–118. https://doi.org/10.1080/00273171.2011.540475.
Visconti, Giancarlo, and José R. Zubizarreta. 2018. “HandlingLimited Overlap in Observational Studies with CardinalityMatching.” Observational Studies 4 (1): 217–49. https://doi.org/10.1353/obs.2018.0012.
Wan, Fei. 2019. “Matched or Unmatched Analyses withPropensity-Scorematched Data?”Statistics in Medicine 38 (2): 289–300. https://doi.org/10.1002/sim.7976.
Wang, Jixian. 2020. “To Use or Not to Use Propensity ScoreMatching?” Pharmaceutical Statistics, August. https://doi.org/10.1002/pst.2051.
Zakrison, T. L., Peter C. Austin, and V. A. McCredie. 2018. “ASystematic Review of Propensity Score Methods in the Acute Care SurgeryLiterature: Avoiding the Pitfalls and Proposing a Set of ReportingGuidelines.” European Journal of Trauma and EmergencySurgery 44 (3): 385–95. https://doi.org/10.1007/s00068-017-0786-6.
Zubizarreta, José R., Ricardo D. Paredes, and Paul R. Rosenbaum. 2014a.“Matching for Balance, Pairing for Heterogeneity in anObservational Study of the Effectiveness of for-Profit andNot-for-Profit High Schools in Chile.” The Annals of AppliedStatistics 8 (1): 204–31. https://doi.org/10.1214/13-AOAS713.
———. 2014b. “Matching for Balance, Pairing for Heterogeneity in anObservational Study of the Effectiveness of for-Profit andNot-for-Profit High Schools in Chile.” TheAnnals of Applied Statistics 8 (1): 204–31. https://doi.org/10.1214/13-AOAS713.
