Austin, P. C. (2007). “The Performance of Different Propensity Score Methods for Estimating Marginal Odds Ratios.” Statistics in Medicine 26:3078–3094.
Austin, P. C. (2009). “Balance Diagnostics for Comparing the Distribution of Baseline Covariates between Treatment Groups in Propensity-Score Matched Samples.” Statistics in Medicine 28:3083–3107.
Austin, P. C. (2011a). “An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.” Multivariate Behavioral Research 46:399–424.
Austin, P. C. (2011b). “Optimal Caliper Widths for Propensity-Score Matching When Estimating Differences in Means and Differences in Proportions in Observational Studies.” Pharmaceutical Statistics 10:150–161.
Austin, P. C. (2014). “A Comparison of 12 Algorithms for Matching on the Propensity Score.” Statistics in Medicine 33:1057–1069.
Austin, P. C., Grootendorst, P., and Anderson, G. M. (2007). “A Comparison of the Ability of Different Propensity Score Models to Balance Measures Variables between Treated and Untreated Subjects: A Monte Carlo Study.” Statistics in Medicine 26:734–753.
Austin, P. C., and Stuart, E. A. (2015a). “Moving towards Best Practice When Using Inverse Probability of Treatment Weighting (IPTW) Using the Propensity Score to Estimate Causal Treatment Effects in Observational Studies.” Statistics in Medicine 34:3661–3679.
Austin, P. C., and Stuart, E. A. (2015b). “The Performance of Inverse Probability of Treatment Weighting and Full Matching on the Propensity Score in the Presence of Model Misspecification When Estimating the Effect of Treatment on Survival Outcomes.” Statistical Methods in Medical Research 26:1654–1670.
Cole, S. R., and Hernán, M. A. (2008). “Constructing Inverse Probability Weights for Marginal Structural Models.” American Journal of Epidemiology 168:656–664.
Crump, R. K., Hotz, V. J., Imbens, G. W., and Mitnik, O. A. (2009). “Dealing with Limited Overlap in Estimation of Average Treatment Effects.” Biometrika 96:187–199.
Faries, D. E., Leon, A. C., Haro, J. M., and Obenchain, R. L., eds. (2010). Analysis of Observational Health Care Data Using SAS. Cary, NC: SAS Institute Inc.
Guo, S., and Fraser, M. W. (2015). Propensity Score Analysis: Statistical Methods and Applications. 2nd ed. Thousand Oaks, CA: Sage Publications.
Hainmueller, J. (2012). “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies.” Political Analysis 20:25–46. https://doi.org/10.1093/pan/mpr025.
Hansen, B. B. (2004). “Full Matching in an Observational Study of Coaching for the SAT.” Journal of the American Statistical Association 99:609–618.
Hill, J., and Reiter, J. P. (2006). “Interval Estimation for Treatment Effects Using Propensity Score Matching.” Statistics in Medicine 25:2230–2256.
Ho, D., Imai, K., King, G., and Stuart, E. A. (2007). “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.” Political Analysis 15:199–236.
Imbens, G. W., and Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. New York: Cambridge University Press.
Liu, W., Kuramoto, S. J., and Stuart, E. A. (2013). “An Introduction to Sensitivity Analysis for Unobserved Confounding in Non-experimental Prevention Research.” Prevention Science 14:570–580.
Lunceford, J. K., and Davidian, M. (2004). “Stratification and Weighting via the Propensity Score in Estimation of Causal Treatment Effects: A Comparative Study.” Statistics in Medicine 23:2937–2960.
Mamdani, M., Sykora, K., Li, P., Normand, S. L., Streiner, D. L., Austin, P. C., Rochon, P. A., and Anderson, G. M. (2005). “Reader’s Guide to Critical Appraisal of Cohort Studies: 2. Assessing Potential for Confounding.” BMJ 330:960–962.
Normand, S.-L. T., Landrum, M. B., Guadagnoli, E., Ayanian, J. Z., Ryan, T. J., Cleary, P. D., and McNeil, B. J. (2001). “Validating Recommendations for Coronary Angiography Following Acute Myocardial Infarction in the Elderly: A Matched Analysis Using Propensity Scores.” Journal of Clinical Epidemiology 54:387–398.
Pan, W., and Bai, H., eds. (2015). Propensity Score Analysis: Fundamentals and Developments. New York: Guilford Press.
Robins, J. M., Hernan, M. A., and Brumback, B. (2000). “Marginal Structural Models and Causal Inference in Epidemiology.” Epidemiology 11:550–560.
Rosenbaum, P. R. (2010). Design of Observational Studies. New York: Springer-Verlag.
Rosenbaum, P. R., and Rubin, D. B. (1983). “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70:41–55.
Rosenbaum, P. R., and Rubin, D. B. (1984). “Reducing Bias in Observational Studies Using Subclassification on the Propensity Score.” Journal of the American Statistical Association 79:516–524.
Rosenbaum, P. R., and Rubin, D. B. (1985). “Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score.” American Statistician 39:33–38.
Rubin, D. B. (1974). “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology 66:688–701.
Rubin, D. B. (1980a). “Bias Reduction Using Mahalanobis-Metric Matching.” Biometrics 36:293–298.
Rubin, D. B. (1980b). “Comment on D. Basu, 'Randomization Analysis of Experimental Data: The Fisher Randomization Test'.” Journal of the American Statistical Association 75:591–593.
Rubin, D. B. (1990). “Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies.” Statistical Science 5:472–480.
Rubin, D. B. (2001). “Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation.” Health Services and Outcomes Research Methodology 2:169–188.
Rubin, D. B. (2005). “Causal Inference Using Potential Outcomes: Design, Modeling, Decisions.” Journal of the American Statistical Association 100:322–331.
Stuart, E. A. (2007). “Estimating Causal Effects Using School-Level Data Sets.” Educational Researcher 36:187–198.
Stuart, E. A. (2010). “Matching Methods for Causal Inference: A Review and a Look Forward.” Statistical Science 25:1–21.
Stuart, E. A., and Ialongo, N. S. (2010). “Matching Methods for Selection of Subjects for Follow-Up.” Multivariate Behavioral Research 45:746–765.
Stuart, E. A., Marcus, S. M., Horvitz-Lennon, M. V., Gibbons, R. D., and Normand, S.-L. T. (2009). “Using Non-experimental Data to Estimate Treatment Effects.” Psychiatric Annals 39:719–728.
Stürmer, T., Wyss, R., Glynn, R. J., and Brookhart, M. A. (2014). “Propensity Scores for Confounder Adjustment When Assessing the Effects of Medical Interventions Using Nonexperimental Study Designs.” Journal of Internal Medicine 275:570–580.
Yue, L. Q., Lu, N., and Xu, Y. (2014). “Designing Premarket Observational Comparative Studies Using Existing Data as Controls: Challenges and Opportunities.” Journal of Biopharmaceutical Statistics 24:994–1010.