References

Abadie, Alberto, and Guido W. Imbens. 2006. “Large Sample Properties of Matching Estimators for Average Treatment Effects.” Econometrica 74 (1): 235–67. https://doi.org/10.1111/j.1468-0262.2006.00655.x.

———. 2008. “On the Failure of the Bootstrap for Matching Estimators.” Econometrica 76 (6): 1537–57. https://doi.org/10.3982/ECTA6474.

———. 2016. “Matching on the Estimated Propensity Score.” Econometrica 84 (2): 781–807. https://doi.org/10.3982/ECTA11293.

Abadie, Alberto, and Jann Spiess. 2020. “Robust Post-Matching Inference.” Journal of the American Statistical Association 0 (ja): 1–37. https://doi.org/10.1080/01621459.2020.1840383.

Alam, Shomoita, Erica E. M. Moodie, and David A. Stephens. 2019. “Should a Propensity Score Model Be Super? The Utility of Ensemble Procedures for Causal Adjustment.” Statistics in Medicine 38 (9): 1690–1702. https://doi.org/10.1002/sim.8075.

Ali, M. Sanni, Rolf H. H. Groenwold, Svetlana V. Belitser, Wiebe R. Pestman, Arno W. Hoes, Kit C. B. Roes, Anthonius de Boer, and Olaf H. Klungel. 2015. “Reporting of Covariate Selection and Balance Assessment in Propensity Score Analysis Is Suboptimal: A Systematic Review.” Journal of Clinical Epidemiology 68 (2): 122–31. https://doi.org/10.1016/j.jclinepi.2014.08.011.

Angeles Resa, Maria de los, and José R. Zubizarreta. 2016. “Evaluation of Subset Matching Methods and Forms of Covariate Balance.” Statistics in Medicine 35 (27): 4961–79. https://doi.org/10.1002/sim.7036.

Arguelles, Gabriel R., Max Shin, Drake G. Lebrun, Christopher J. DeFrancesco, Peter D. Fabricant, and Keith D. Baldwin. 2022. “A Systematic Review of Propensity Score Matching in the Orthopedic Literature.” HSS Journal, April, 15563316221082632. https://doi.org/10.1177/15563316221082632.

Austin, Peter C. 2009. “Type I Error Rates, Coverage of Confidence Intervals, and Variance Estimation in Propensity-Score Matched Analyses.” The International Journal of Biostatistics 5 (1). https://doi.org/10.2202/1557-4679.1146.

———. 2011a. “Optimal Caliper Widths for Propensity-Score Matching When Estimating Differences in Means and Differences in Proportions in Observational Studies.” Pharmaceutical Statistics 10 (2): 150–61. https://doi.org/10.1002/pst.433.

———. 2011b. “An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.” Multivariate Behavioral Research 46 (3): 399–424. https://doi.org/10.1080/00273171.2011.568786.

———. 2014. “A Comparison of 12 Algorithms for Matching on the Propensity Score.” Statistics in Medicine 33 (6): 1057–69. https://doi.org/10.1002/sim.6004.

———. 2019. “Assessing Covariate Balance When Using the Generalized Propensity Score with Quantitative or Continuous Exposures.” Statistical Methods in Medical Research 28 (5): 1365–77. https://doi.org/10.1177/0962280218756159.

———. 2022. “Bootstrap Vs Asymptotic Variance Estimation When Using Propensity Score Weighting with Continuous and Binary Outcomes.” Statistics in Medicine 41 (22): 4426–43. https://doi.org/10.1002/sim.9519.

Austin, Peter C., and Dylan S. Small. 2014. “The Use of Bootstrapping When Using Propensity-Score Matching Without Replacement: A Simulation Study.” Statistics in Medicine 33 (24): 4306–19. https://doi.org/10.1002/sim.6276.

Austin, Peter C., and Elizabeth A. Stuart. 2015a. “Optimal Full Matching for Survival Outcomes: A Method That Merits More Widespread Use.” Statistics in Medicine 34 (30): 3949–67. https://doi.org/10.1002/sim.6602.

———. 2015b. “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 (28): 3661–79. https://doi.org/10.1002/sim.6607.

———. 2017a. “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 (4): 1654–70. https://doi.org/10.1177/0962280215584401.

———. 2017b. “Estimating the Effect of Treatment on Binary Outcomes Using Full Matching on the Propensity Score.” Statistical Methods in Medical Research 26 (6): 2505–25. https://doi.org/10.1177/0962280215601134.

Benedetto, Umberto, Stuart J Head, Gianni D Angelini, and Eugene H Blackstone. 2018. “Statistical Primer: Propensity Score Matching and Its Alternatives.” European Journal of Cardio-Thoracic Surgery 53 (6): 1112–17. https://doi.org/10.1093/ejcts/ezy167.

Ben-Michael, Eli, Avi Feller, David A. Hirshberg, and José R. Zubizarreta. 2021. “The Balancing Act in Causal Inference.” arXiv:2110.14831 [Stat], October. https://arxiv.org/abs/2110.14831.

Bramante, Carolyn T., Steven G. Johnson, Victor Garcia, Michael D. Evans, Jeremy Harper, Kenneth J. Wilkins, Jared D. Huling, et al. 2022. “Diabetes Medications and Associations with Covid-19 Outcomes in the N3C Database: A National Retrospective Cohort Study.” Edited by Surasak Saokaew. PLOS ONE 17 (11): e0271574. https://doi.org/10.1371/journal.pone.0271574.

Brookhart, M. Alan, Til Stürmer, Robert J. Glynn, Jeremy Rassen, and Sebastian Schneeweiss. 2010. “Confounding Control in Healthcare Database Research: Challenges and Potential Approaches.” Medical Care 48 (6): S114–20. https://doi.org/10.1097/MLR.0b013e3181dbebe3.

Cafri, Guy, and Peter C. Austin. 2023. “Variance Estimation of the Risk Difference When Using Propensity-Score Matching and Weighting with Time-to-Event Outcomes.” Pharmaceutical Statistics, May, pst.2317. https://doi.org/10.1002/pst.2317.

Caliendo, Marco, and Sabine Kopeinig. 2008. “Some Practical Guidance for the Implementation of Propensity Score Matching.” Journal of Economic Surveys 22 (1): 31–72. https://doi.org/10.1111/j.1467-6419.2007.00527.x.

Carpenter, James, and John Bithell. 2000. “Bootstrap Confidence Intervals: When, Which, What? A Practical Guide for Medical Statisticians.” Statistics in Medicine 19 (9): 1141–64. https://doi.org/10.1002/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F.

Cham, Heining, and Stephen G. West. 2016. “Propensity Score Analysis with Missing Data.” Psychological Methods 21 (3): 427–45. https://doi.org/10.1037/met0000076.

Chan, Kwun Chuen Gary, Sheung Chi Phillip Yam, and Zheng Zhang. 2016. “Globally Efficient Non-Parametric Inference of Average Treatment Effects by Empirical Balancing Calibration Weighting.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78 (3): 673–700. https://doi.org/10.1111/rssb.12129.

Chattopadhyay, Ambarish, Christopher H. Hase, and José R. Zubizarreta. 2020. “Balancing Vs Modeling Approaches to Weighting in Practice.” Statistics in Medicine 39 (24): 3227–54. https://doi.org/10.1002/sim.8659.

Cohn, Eric R., and José R. Zubizarreta. 2022. “Profile Matching for the Generalization and Personalization of Causal Inferences.” Epidemiology 33 (5): 678. https://doi.org/10.1097/EDE.0000000000001517.

Cole, Stephen R., and Constantine E. Frangakis. 2009. “The Consistency Statement in Causal Inference: A Definition or an Assumption?” Epidemiology 20 (1): 3–5. https://doi.org/10.1097/EDE.0b013e31818ef366.

Connors, Alfred F, Neal V Dawson, Frank E Harrell, Douglas Wagner, Norman Desbiens, Lee Goldman, Albert W Wu, et al. 1996. “The Effectiveness of Right Heart Catheterization in the Initial Care of Critically III Patients.” JAMA: The Journal of the American Medical Association 276 (11): 889. https://doi.org/10.1001/jama.1996.03540110043030.

D’Agostino McGowan, Lucy. 2022. “Sensitivity Analyses for Unmeasured Confounders.” Current Epidemiology Reports 9 (4): 361–75. https://doi.org/10.1007/s40471-022-00308-6.

Daniel, Rhian M. 2018. “Double Robustness.” In, 1–14. American Cancer Society. https://doi.org/10.1002/9781118445112.stat08068.

Desai, Rishi J., Kenneth J. Rothman, Brian .T Bateman, Sonia Hernandez-Diaz, and Krista F. Huybrechts. 2017. “A Propensity-Score-Based Fine Stratification Approach for Confounding Adjustment When Exposure Is Infrequent:” Epidemiology 28 (2): 249–57. https://doi.org/10.1097/EDE.0000000000000595.

Diamond, Alexis, and Jasjeet S. Sekhon. 2013. “Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.” Review of Economics and Statistics 95 (3): 932945. https://doi.org/10.1162/REST_a_00318.

Dong, Jing, Junni L Zhang, Shuxi Zeng, and Fan Li. 2020. “Subgroup Balancing Propensity Score.” Statistical Methods in Medical Research 29 (3): 659–76. https://doi.org/10.1177/0962280219870836.

Efron, B., and R. Tibshirani. 1986. “Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy.” Statistical Science 1 (1): 54–75. https://www.jstor.org/stable/2245500.

Elwert, Felix, and Christopher Winship. 2014. “Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable.” Annual Review of Sociology 40 (1): 31–53. https://doi.org/10.1146/annurev-soc-071913-043455.

Fogarty, Colin B., Mark E. Mikkelsen, David F. Gaieski, and Dylan S. Small. 2016. “Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality.” Journal of the American Statistical Association 111 (514): 447–58. https://doi.org/10.1080/01621459.2015.1112802.

Fortin, Stephen P., and Martijn Schuemie. 2022. “Indirect Covariate Balance and Residual Confounding: An Applied Comparison of Propensity Score Matching and Cardinality Matching.” Pharmacoepidemiology and Drug Safety 31 (12): 1242–52. https://doi.org/10.1002/pds.5510.

Gabriel, Erin E., Michael C. Sachs, Torben Martinussen, Ingeborg Waernbaum, Els Goetghebeur, Stijn Vansteelandt, and Arvid Sjölander. 2024. “Inverse Probability of Treatment Weighting with Generalized Linear Outcome Models for Doubly Robust Estimation.” Statistics in Medicine 43 (3): 534–47. https://doi.org/10.1002/sim.9969.

Gayat, Etienne, Matthieu Resche-Rigon, Jean-Yves Mary, and Raphaël Porcher. 2012. “Propensity Score Applied to Survival Data Analysis Through Proportional Hazards Models: A Monte Carlo Study.” Pharmaceutical Statistics 11 (3): 222–29. https://doi.org/10.1002/pst.537.

Green, Kerry M., and Elizabeth A. Stuart. 2014. “Examining Moderation Analyses in Propensity Score Methods: Application to Depression and Substance Use.” Journal of Consulting and Clinical Psychology, Advances in data analytic methods, 82 (5): 773–83. https://doi.org/10.1037/a0036515.

Greenland, Sander, Judea Pearl, and James M. Robins. 1999. “Causal Diagrams for Epidemiologic Research.” Epidemiology 10 (1): 37–48. https://www.jstor.org/stable/3702180.

Greifer, Noah. 2020. Cobalt: Covariate Balance Tables and Plots. https://CRAN.R-project.org/package=cobalt.

Greifer, Noah, and Elizabeth A Stuart. 2021a. “Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist’s Toolbox.” Epidemiologic Reviews, June, mxab003. https://doi.org/10.1093/epirev/mxab003.

Greifer, Noah, and Elizabeth A. Stuart. 2021b. “Choosing the Estimand When Matching or Weighting in Observational Studies.” arXiv:2106.10577 [Stat], June. https://arxiv.org/abs/2106.10577.

Gu, Xing Sam, and Paul R. Rosenbaum. 1993. “Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms.” Journal of Computational and Graphical Statistics 2 (4): 405. https://doi.org/10.2307/1390693.

Hainmueller, J. 2012. “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies.” Political Analysis 20 (1): 25–46. https://doi.org/10.1093/pan/mpr025.

Han, Shasha, and Xiao-Hua Zhou. 2023. “Defining Estimands in Clinical Trials: A Unified Procedure.” Statistics in Medicine, March, sim.9702. https://doi.org/10.1002/sim.9702.

Haneuse, Sebastien, Tyler J. VanderWeele, and David Arterburn. 2019. “Using the e-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies.” JAMA 321 (6): 602–3. https://doi.org/10.1001/jama.2018.21554.

Hansen, Ben B, and Stephanie Olsen Klopfer. 2006. “Optimal Full Matching and Related Designs via Network Flows.” Journal of Computational and Graphical Statistics 15 (3): 609–27. https://doi.org/10.1198/106186006X137047.

Harder, Valerie S., Elizabeth A. Stuart, and James C. Anthony. 2010. “Propensity Score Techniques and the Assessment of Measured Covariate Balance to Test Causal Associations in Psychological Research.” Psychological Methods 15 (3): 234–49. https://doi.org/10.1037/a0019623.

Hernán, Miguel A. 2010. “The Hazards of Hazard Ratios.” Epidemiology (Cambridge, Mass.) 21 (1): 13–15. https://doi.org/10.1097/EDE.0b013e3181c1ea43.

Hernán, Miguel A., and James M. Robins. 2006a. “Instruments for Causal Inference: An Epidemiologist’s Dream?” Epidemiology 17 (4): 360–72. https://doi.org/10.1097/01.ede.0000222409.00878.37.

Hernán, Miguel A, and James M Robins. 2020. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2020/01/ci_hernanrobins_21jan20.pdf.

Hernán, Miguel A, and James M. Robins. 2006b. “Estimating Causal Effects from Epidemiological Data.” Journal of Epidemiology and Community Health (1979-) 60 (7): 578–86. http://www.jstor.org/stable/40795098.

Hernán, Miguel A, and S L Taubman. 2008. “Does Obesity Shorten Life? The Importance of Well-Defined Interventions to Answer Causal Questions.” International Journal of Obesity 32 (S3): S8–14. https://doi.org/10.1038/ijo.2008.82.

Hill, Jennifer, and Jerome P. Reiter. 2006. “Interval Estimation for Treatment Effects Using Propensity Score Matching.” Statistics in Medicine 25 (13): 2230–56. https://doi.org/10.1002/sim.2277.

Hill, Jennifer, Christopher Weiss, and Fuhua Zhai. 2011. “Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative.” Multivariate Behavioral Research 46 (3): 477–513. https://doi.org/10.1080/00273171.2011.570161.

Hirano, Keisuke, and Guido W. Imbens. 2005. “The Propensity Score with Continuous Treatments.” In, edited by Andrew Gelman and Xiao-Li Meng, 73–84. Chichester, UK: John Wiley & Sons, Ltd. https://doi.org/10.1002/0470090456.ch7.

Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. 2007. “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.” Political Analysis 15 (3): 199–236. https://doi.org/10.1093/pan/mpl013.

Hong, Guanglei. 2010. “Marginal Mean Weighting Through Stratification: Adjustment for Selection Bias in Multilevel Data.” Journal of Educational and Behavioral Statistics 35 (5): 499–531. https://doi.org/10.3102/1076998609359785.

Huber, Martin. 2015. “Causal Pitfalls in the Decomposition of Wage Gaps.” Journal of Business & Economic Statistics 33 (2): 179–91. https://doi.org/10.1080/07350015.2014.937437.

Huling, Jared D., Noah Greifer, and Guanhua Chen. 2023. “Independence Weights for Causal Inference with Continuous Treatments.” Journal of the American Statistical Association 0 (ja): 1–25. https://doi.org/10.1080/01621459.2023.2213485.

Huling, Jared D., and Simon Mak. 2024. “Energy Balancing of Covariate Distributions.” Journal of Causal Inference 12 (1). https://doi.org/10.1515/jci-2022-0029.

———. n.d. “Energy Balancing of Covariate Distributions.” https://doi.org/10.48550/arXiv.2004.13962.

Iacus, Stefano M., Gary King, and Giuseppe Porro. 2011. “Multivariate Matching Methods That Are Monotonic Imbalance Bounding.” Journal of the American Statistical Association 106 (493): 345–61. https://doi.org/10.1198/jasa.2011.tm09599.

———. 2012. “Causal Inference Without Balance Checking: Coarsened Exact Matching.” Political Analysis 20 (1): 1–24. https://doi.org/10.1093/pan/mpr013.

Imai, Kosuke, Gary King, and Elizabeth A. Stuart. 2008. “Misunderstandings Between Experimentalists and Observationalists about Causal Inference.” Journal of the Royal Statistical Society. Series A (Statistics in Society) 171 (2): 481–502. https://doi.org/10.1111/j.1467-985X.2007.00527.x.

Imai, Kosuke, and Marc Ratkovic. 2014. “Covariate Balancing Propensity Score.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (1): 243263. https://doi.org/10.1111/rssb.12027.

Imai, Kosuke, and David A. Van Dyk. 2004. “Causal Inference with General Treatment Regimes: Generalizing the Propensity Score.” Journal of the American Statistical Association 99 (467): 854–66. https://www.jstor.org/stable/27590455.

Imbens, Guido W. 2000. “The Role of the Propensity Score in Estimating Dose-Response Functions.” Biometrika 87 (3): 706–10. https://www.jstor.org/stable/2673642.

Ioannidis, John P. A., Yuan Jin Tan, and Manuel R. Blum. 2019. “Limitations and Misinterpretations of e-Values for Sensitivity Analyses of Observational Studies.” Annals of Internal Medicine 170 (2): 108–11. https://doi.org/10.7326/M18-2159.

Kahan, Brennan C, Suzie Cro, Fan Li, and Michael O Harhay. 2023. “Eliminating Ambiguous Treatment Effects Using Estimands.” American Journal of Epidemiology, February, kwad036. https://doi.org/10.1093/aje/kwad036.

Kang, Joseph D. Y., and Joseph L. Schafer. 2007. “Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data.” Statistical Science 22 (4): 523–39. https://doi.org/10.1214/07-STS227.

King, Gary, and Richard Nielsen. 2019. “Why Propensity Scores Should Not Be Used for Matching.” Political Analysis, May, 1–20. https://doi.org/10.1017/pan.2019.11.

King, Gary, and Langche Zeng. 2006. “The Dangers of Extreme Counterfactuals.” Political Analysis 14 (2): 131–59. https://doi.org/10.1093/pan/mpj004.

Kush, Joseph M., Elise T. Pas, Rashelle J. Musci, and Catherine P. Bradshaw. 2022. “Covariate Balance for Observational Effectiveness Studies: A Comparison of Matching and Weighting.” Journal of Research on Educational Effectiveness 0 (0): 1–24. https://doi.org/10.1080/19345747.2022.2110545.

Lee, Brian K., Justin Lessler, and Elizabeth A. Stuart. 2010. “Improving Propensity Score Weighting Using Machine Learning.” Statistics in Medicine 29 (3): 337–46. https://doi.org/10.1002/sim.3782.

Leyrat, Clémence, Shaun R Seaman, Ian R White, Ian Douglas, Liam Smeeth, Joseph Kim, Matthieu Resche-Rigon, James R Carpenter, and Elizabeth J Williamson. 2019. “Propensity Score Analysis with Partially Observed Covariates: How Should Multiple Imputation Be Used?” Statistical Methods in Medical Research 28 (1): 3–19. https://doi.org/10.1177/0962280217713032.

Li, Fan, and Fan Li. 2019. “Propensity Score Weighting for Causal Inference with Multiple Treatments.” The Annals of Applied Statistics 13 (4): 2389–2415. https://doi.org/10.1214/19-AOAS1282.

Li, Liang, and Tom Greene. 2013. “A Weighting Analogue to Pair Matching in Propensity Score Analysis.” The International Journal of Biostatistics 9 (2). https://doi.org/10.1515/ijb-2012-0030.

Li, Yan, and Liang Li. 2021. “Propensity Score Analysis Methods with Balancing Constraints: A Monte Carlo Study.” Statistical Methods in Medical Research 30 (4): 1119–42. https://doi.org/10.1177/0962280220983512.

Liang, Kung-Yee, and Scott L. Zeger. 1986. “Longitudinal Data Analysis Using Generalized Linear Models.” Biometrika 73 (1): 13–22. https://doi.org/10.1093/biomet/73.1.13.

Lopez, Michael J., and Roee Gutman. 2017. “Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas.” Statistical Science 32 (3): 432–54. https://doi.org/10.1214/17-STS612.

Lunceford, Jared K., and Marie Davidian. 2004. “Stratification and Weighting via the Propensity Score in Estimation of Causal Treatment Effects: A Comparative Study.” Statistics in Medicine 23 (19): 29372960. https://doi.org/10.1002/sim.1903.

MacKinnon, James G, and Halbert White. 1985. “Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties.” Journal of Econometrics 29 (3): 305–25. https://doi.org/10.1016/0304-4076(85)90158-7.

Mao, Huzhang, Liang Li, and Tom Greene. 2018. “Propensity Score Weighting Analysis and Treatment Effect Discovery.” Statistical Methods in Medical Research, June, 096228021878117. https://doi.org/10.1177/0962280218781171.

Matthay, Ellicott C., Erin Hagan, Laura M. Gottlieb, May Lynn Tan, David Vlahov, Nancy E. Adler, and M. Maria Glymour. 2020. “Alternative Causal Inference Methods in Population Health Research: Evaluating Tradeoffs and Triangulating Evidence.” SSM - Population Health 10 (April): 100526. https://doi.org/10.1016/j.ssmph.2019.100526.

McCaffrey, Daniel F., Beth Ann Griffin, Daniel Almirall, Mary Ellen Slaughter, Rajeev Ramchand, and Lane F. Burgette. 2013. “A Tutorial on Propensity Score Estimation for Multiple Treatments Using Generalized Boosted Models.” Statistics in Medicine 32 (19): 3388–3414. https://doi.org/10.1002/sim.5753.

McCaffrey, Daniel F., Greg Ridgeway, and Andrew R. Morral. 2004. “Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies.” Psychological Methods 9 (4): 403–25. https://doi.org/10.1037/1082-989X.9.4.403.

Ming, Kewei, and Paul R. Rosenbaum. 2000. “Substantial Gains in Bias 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.

Niknam, Bijan A., and Jose R. Zubizarreta. 2022. “Using Cardinality Matching to Design Balanced and Representative Samples for Observational Studies.” JAMA 327 (2): 173–74. https://doi.org/10.1001/jama.2021.20555.

Pak, Kyongsun, Hajime Uno, Dae Hyun Kim, Lu Tian, Robert C. Kane, Masahiro Takeuchi, Haoda Fu, Brian Claggett, and Lee-Jen Wei. 2017. “Interpretability of Cancer Clinical Trial Results Using Restricted Mean Survival Time as an Alternative to the Hazard Ratio.” JAMA Oncology 3 (12): 1692. https://doi.org/10.1001/jamaoncol.2017.2797.

Pishgar, Farhad, Noah Greifer, Clémence Leyrat, and Elizabeth Stuart. 2021. “MatchThem:: Matching and Weighting After Multiple Imputation.” The R Journal 13 (2): 292305. https://doi.org/10.32614/RJ-2021-073.

Rassen, Jeremy A., Abhi A. Shelat, Jessica Myers, Robert J. Glynn, Kenneth J. Rothman, and Sebastian Schneeweiss. 2012. “One-to-Many Propensity Score Matching in Cohort Studies.” Pharmacoepidemiology and Drug Safety 21 (S2): 69–80. https://doi.org/10.1002/pds.3263.

Reifeis, Sarah A., and Michael G. Hudgens. 2020. “On Variance of the Treatment Effect in the Treated Using Inverse Probability Weighting.” arXiv:2011.11874 [Stat], November. http://arxiv.org/abs/2011.11874.

Ridgeway, Greg. 2006. “Assessing the Effect of Race Bias in Post-Traffic Stop Outcomes Using Propensity Scores.” Journal of Quantitative Criminology 22 (1): 1–29. https://doi.org/10.1007/s10940-005-9000-9.

Ripollone, John E., Krista F. Huybrechts, Kenneth J. Rothman, Ryan E. Ferguson, and Jessica M. Franklin. 2018. “Implications of the Propensity Score Matching Paradox in Pharmacoepidemiology.” American Journal of Epidemiology 187 (9): 1951–61. https://doi.org/10.1093/aje/kwy078.

Robins, James M., Miguel Ángel Hernán, and Babette Brumback. 2000. “Marginal Structural Models and Causal Inference in Epidemiology.” Epidemiology 11 (5): 550–60. https://doi.org/10.1097/00001648-200009000-00011.

Rosenbaum, Paul R. 2007. “Interference Between Units in Randomized Experiments.” Journal of the American Statistical Association 102 (477): 191–200. https://doi.org/10.1198/016214506000001112.

Rosenbaum, Paul R. 2010. Design of Observational Studies. Springer Series in Statistics. New York: Springer.

Rosenbaum, Paul R., and Donald B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70 (1): 41–55. https://doi.org/10.1093/biomet/70.1.41.

———. 1984. “Reducing Bias in Observational Studies Using Subclassification on the Propensity Score.” Journal of the American Statistical Association 79 (387): 516–24. https://doi.org/10.2307/2288398.

———. 1985. “The Bias Due to Incomplete Matching.” Biometrics 41 (1): 103–16. https://doi.org/10.2307/2530647.

Rubin, Donald B. 1973. “Matching to Remove Bias in Observational Studies.” Biometrics 29 (1): 159–83. https://doi.org/10.2307/2529684.

———. 1980. “Bias Reduction Using Mahalanobis-Metric Matching.” Biometrics 36 (2): 293–98. https://doi.org/10.2307/2529981.

———. 2004. Multiple Imputation for Nonresponse in Surveys. Wiley Classics Library. Hoboken, N.J: Wiley-Interscience.

Sävje, F. 2022. “On the Inconsistency of Matching Without Replacement.” Biometrika 109 (2): 551–58. https://doi.org/10.1093/biomet/asab035.

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.

Shadish, William R., and Peter M. Steiner. 2010. “A Primer on Propensity Score Analysis.” Newborn and Infant Nursing Reviews, Quantitative research methodology, 10 (1): 19–26. https://doi.org/10.1053/j.nainr.2009.12.010.

Sharma, Mayur, Truong H. Do, Elise F. Palzer, Jared D. Huling, and Clark C. Chen. 2023. “Comparable Safety Profile Between Neuro-Oncology Procedures Involving Stereotactic Needle Biopsy (SNB) Followed by Laser Interstitial Thermal Therapy (LITT) and LITT Alone Procedures.” Journal of Neuro-Oncology 162 (1): 147–56. https://doi.org/10.1007/s11060-023-04275-w.

Snowden, Jonathan M., Sherri Rose, and Kathleen M. Mortimer. 2011. “Implementation of G-Computation on a Simulated Data Set: Demonstration of a Causal Inference Technique.” American Journal of Epidemiology 173 (7): 731–38. https://doi.org/10.1093/aje/kwq472.

Stefanski, Leonard A., and Dennis D. Boos. 2002. “The Calculus of m-Estimation.” The American Statistician 56 (1): 29–38. https://doi.org/10.1198/000313002753631330.

Stensrud, Mats J., and Miguel A. Hernán. 2020. “Why Test for Proportional Hazards?” JAMA 323 (14): 1401–2. https://doi.org/10.1001/jama.2020.1267.

Strasser, Zachary H., Noah Greifer, Aboozar Hadavand, Shawn N. Murphy, and Hossein Estiri. 2022. “Estimates of SARS-CoV-2 Omicron BA.2 Subvariant Severity in New England.” JAMA Network Open 5 (10): e2238354. https://doi.org/10.1001/jamanetworkopen.2022.38354.

Stuart, Elizabeth A. 2010. “Matching Methods for Causal Inference: A Review and a Look Forward.” Statistical Science 25 (1): 1–21. https://doi.org/10.1214/09-STS313.

Stuart, Elizabeth A., and Kerry M. Green. 2008. “Using Full Matching to Estimate Causal Effects in Nonexperimental Studies: Examining the Relationship Between Adolescent Marijuana Use and Adult Outcomes.” Developmental Psychology, New methods for new questions in developmental psychology, 44 (2): 395–406. https://doi.org/10.1037/0012-1649.44.2.395.

Stuart, Elizabeth A., Brian K. Lee, and Finbarr P. Leacy. 2013. “Prognostic Score-Based Balance Measures Can Be a Useful Diagnostic for Propensity Score Methods in Comparative Effectiveness Research.” Journal of Clinical Epidemiology 66 (8): S84. https://doi.org/10.1016/j.jclinepi.2013.01.013.

Tchetgen, Eric J Tchetgen, and Tyler J VanderWeele. 2012. “On Causal Inference in the Presence of Interference.” Statistical Methods in Medical Research 21 (1): 55–75. https://doi.org/10.1177/0962280210386779.

Thoemmes, Felix J., and Anthony D. Ong. 2016. “A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models.” Emerging Adulthood 4 (1): 40–59. https://doi.org/10.1177/2167696815621645.

VanderWeele, Tyler J. 2009. “On the Distinction Between Interaction and Effect Modification.” Epidemiology 20 (6): 863–71. https://www.jstor.org/stable/25662776.

———. 2019. “Principles of Confounder Selection.” European Journal of Epidemiology 34 (3): 211–19. https://doi.org/10.1007/s10654-019-00494-6.

VanderWeele, Tyler J., and Peng Ding. 2017. “Sensitivity Analysis in Observational Research: Introducing the E-Value.” Annals of Internal Medicine 167 (4): 268. https://doi.org/10.7326/M16-2607.

VanderWeele, Tyler J., Maya B. Mathur, and Peng Ding. 2019. “Correcting Misinterpretations of the e-Value.” Annals of Internal Medicine 170 (2): 131–32. https://doi.org/10.7326/M18-3112.

Vansteelandt, Stijn, and Niels Keiding. 2011. “Invited Commentary: G-Computationlost in Translation?” American Journal of Epidemiology 173 (7): 739–42. https://doi.org/10.1093/aje/kwq474.

Visconti, Giancarlo, and José R. Zubizarreta. 2018. “Handling Limited Overlap in Observational Studies with Cardinality Matching.” Observational Studies 4 (1): 217–49. https://doi.org/10.1353/obs.2018.0012.

Wan, Fei. 2019. “Matched or Unmatched Analyses with Propensity-Scorematched Data?” Statistics in Medicine 38 (2): 289–300. https://doi.org/10.1002/sim.7976.

Westreich, Daniel, and Stephen R. Cole. 2010. “Invited Commentary: Positivity in Practice.” American Journal of Epidemiology 171 (6): 674–77. https://doi.org/10.1093/aje/kwp436.

Westreich, Daniel, and Sander Greenland. 2013. “The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients.” American Journal of Epidemiology 177 (4): 292–98. https://doi.org/10.1093/aje/kws412.

Wu, Xiao, Fabrizia Mealli, Marianthi-Anna Kioumourtzoglou, Francesca Dominici, and Danielle Braun. 2022. “Matching on Generalized Propensity Scores with Continuous Exposures.” Journal of the American Statistical Association 0 (0): 1–29. https://doi.org/10.1080/01621459.2022.2144737.

Zakrison, T. L., Peter C. Austin, and V. A. McCredie. 2018. “A Systematic Review of Propensity Score Methods in the Acute Care Surgery Literature: Avoiding the Pitfalls and Proposing a Set of Reporting Guidelines.” European Journal of Trauma and Emergency Surgery 44 (3): 385–95. https://doi.org/10.1007/s00068-017-0786-6.

Zhao, Qingyuan, and Daniel Percival. 2017. “Entropy Balancing Is Doubly Robust.” Journal of Causal Inference 5 (1). https://doi.org/10.1515/jci-2016-0010.

Zhu, Yeying, Donna L. Coffman, and Debashis Ghosh. 2015. “A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments.” Journal of Causal Inference 3 (1). https://doi.org/10.1515/jci-2014-0022.

Zubizarreta, José R. 2015. “Stable Weights That Balance Covariates for Estimation with Incomplete Outcome Data.” Journal of the American Statistical Association 110 (511): 910–22. https://doi.org/10.1080/01621459.2015.1023805.

Zubizarreta, José R., Ricardo D. Paredes, and Paul R. Rosenbaum. 2014. “Matching for Balance, Pairing for Heterogeneity in an Observational Study of the Effectiveness of for-Profit and Not-for-Profit High Schools in Chile.” The Annals of Applied Statistics 8 (1): 204–31. https://doi.org/10.1214/13-AOAS713.