References
- Anglin, G., Peikes, D., Harrington, M., Ghosh, A., Geonnotti, K., O’Malley, A., and Stacy, E. A. Dale (2019), “Independent Evaluation of Comprehensive Primary Care Plus (CPC+): First Annual Report, Supplemental Volume,” Technical report, Princeton, NJ: Mathematica Policy Research.
- Austin, P. C., and Stuart, E. A. (2015), “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. DOI: https://doi.org/10.1002/sim.6607.
- Breiman, L. (2001), “Random Forests,” Machine Learning, 45, 5–32. DOI: https://doi.org/10.1023/A:1010933404324.
- Busso, M., DiNardo, J., and McCrary, J. (2014), “New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators,”. Review of Economics and Statistics, 96, 885–897. DOI: https://doi.org/10.1162/REST_a_00431.
- Cattaneo, M. D. (2010), “Efficient Semiparametric Estimation of Multi-valued Treatment Effects Under Ignorability,” Journal of Econometrics, 155, 138–154. DOI: https://doi.org/10.1016/j.jeconom.2009.09.023.
- Cole, S. R., and Hernán, M. A. (2008), “Constructing Inverse Probability Weights for Marginal Structural Models,” American Journal of Epidemiology, 168, 656–664. DOI: https://doi.org/10.1093/aje/kwn164.
- Crump, R. K., Hotz, V. J. G., Imbens, W., and Mitnik, O. A. (2009), “Dealing with Limited Overlap in Estimation of Average Treatment Effects,” Biometrika, 96, 187–199. DOI: https://doi.org/10.1093/biomet/asn055.
- Dehejia, R. H., and Wahba, S. (2002), “Propensity Score-Matching Methods for Nonexperimental Causal Studies,” Review of Economics and Statistics, 84, 151–161. DOI: https://doi.org/10.1162/003465302317331982.
- Elliott, M. R., and Little, R. J. (2000), “Model-Based Alternatives to Trimming Survey Weights,” Journal of Official Statistics, 16, 191–209.
- Fong, C., Hazlett, C., and Imai, K. (2018), “Covariate Balancing Propensity Score for a Continuous Treatment: Application to the Efficacy of Political Advertisements,” The Annals of Applied Statistics, 12, 156–177. DOI: https://doi.org/10.1214/17-AOAS1101.
- Fong, C., Ratkovic, M., Imai, K., Hazlett, C., Yang, X., and Peng, S. (2018), “CBPS: Covariate Balancing Propensity Score,” available at https://CRAN.R-project.org/package=CBPS.
- Garrido, M. M., Kelley, A. S., Paris, J., Roza, K., Meier, D. E., Morrison, R. S., and Aldridge, M. D. (2014), “Methods for Constructing and Assessing Propensity Scores,” Health Services Research, 49, 1701–1720. DOI: https://doi.org/10.1111/1475-6773.12182.
- Golinelli, D., Ridgeway, G., Rhoades, H., Tucker, J., and Wenzel, S. (2012), “Bias and Variance Trade-offs When Combining Propensity Score Weighting and Regression: With an Application to HIV Status and Homeless Men,” Health Services and Outcomes Research Methodology, 12, 104–118. DOI: https://doi.org/10.1007/s10742-012-0090-1.
- Hainmueller, J. (2012), “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies,” Political Analysis, 20, 25–46. DOI: https://doi.org/10.1093/pan/mpr025.
- Hansen, B. (2004), “Full Matching in an Observational Study of Coaching for the Sat,” Journal of the American Statistical Association, 99, 609–618. DOI: https://doi.org/10.1198/016214504000000647.
- Hansen, L. P. (1982), “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, 50, 1029–1054. DOI: https://doi.org/10.2307/1912775.
- Hirano, K., and Imbens, G. W. (2001), “Estimation of Causal Effects Using Propensity Score Weighting: An Application to Data on Right Heart Catheterization,” Health Services and Outcomes Research Methodology, 2, 259–278.
- Hirano, K., Imbens, G. W., and Ridder, G. (2003), “Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,” Econometrica, 71, 1161–1189. DOI: https://doi.org/10.1111/1468-0262.00442.
- Horvitz, D. G., and Thompson, D. J. (1952), “A Generalization of Sampling Without Replacement from a Finite Universe,” Journal of the American Statistical Association, 47, 663–685. DOI: https://doi.org/10.1080/01621459.1952.10483446.
- Imai, K., and Ratkovic, M. (2014),”Covariate Balancing Propensity Score,” Journal of the Royal Statistical Society, Series B, 76, 243–263. DOI: https://doi.org/10.1111/rssb.12027.
- Imbens, G. W. (2015), “Matching Methods in Practice: Three Examples,” Journal of Human Resources, 50, 373–419. DOI: https://doi.org/10.3368/jhr.50.2.373.
- Imbens, G. W., and Rubin, D. B. (2015), Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, New York: Cambridge University Press.
- Imbens, G. W., and Wooldridge, J. M. (2009), “Recent Developments in the Econometrics of Program Evaluation,” Journal of Economic Literature, 47, 5–86. DOI: https://doi.org/10.1257/jel.47.1.5.
- Kang, J. D. Y., and Schafer, J. L. (2007), “Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data,” Statistical Science, 22, 523–539. DOI: https://doi.org/10.1214/07-STS227.
- Kish, L. (1992), “Weighting for Unequal Pi,” Journal of Official Statistics, 8, 183–200.
- Kranker, K. (2019), “PSWEIGHT: IPW- and CBPS-type Propensity Score Reweighting, with Various Extensions,” available at https://ideas.repec.org/c/boc/bocode/s458657.html and https://github.com/kkranker/psweight.
- Lee, B. K., Lessler, J., and Stuart, E. A. (2009), “Improving Propensity Score Weighting Using Machine Learning,” Statistics in Medicine, 29, 337–346. DOI: https://doi.org/10.1002/sim.3782.
- Lee, B. K., Lessler, J., and Stuart, E. A. (2011), “Weight Trimming and Propensity Score,” PLoS One, 6, e18174. DOI: https://doi.org/10.1371/journal.pone.0018174.
- Peikes, D., Anglin, G., Harrington, M., Ghosh, A., Geonnotti, K., O’Malley, A., Dale, S., Blue, L., Orzol, S., Machta, R., Laird, J., Singh, P., Mutti, A., Morrison, N., Oh, G., Duda, N., Haile, G., Kim, M.-Y., Holt, J., Shurrer, J., Pu, J., Peterson, G., Vollmer, L., Kranker, K., Crosson, J., Hoag, S., Petersen, D., Finucane, M., Urato, C., and Brown, R. (2019), “Evaluation of the Comprehensive Primary Care Plus Initiative: First Annual Report, Appendices to the Supplemental Volume,” Technical report, Princeton, NJ: Mathematica Policy Research.
- Robins, J., Sued, M., Lei-Gomez, Q., and Rotnitzky, A. (2007), “Comment: Performance of Double-Robust Estimators When “Inverse Probability” Weights Are Highly Variable,” Statistical Science, 22, 544–559. DOI: https://doi.org/10.1214/07-STS227D.
- Rosenbaum, P. R. (1987), “Model-based Direct Adjustment,” Journal of the American Statistical Association, 82, 387–394. DOI: https://doi.org/10.1080/01621459.1987.10478441.
- Rosenbaum, P. R. (1989), “Optimal Matching for Observational Studies,” Journal of the American Statistical Association, 84, 1024–1032.
- 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. DOI: https://doi.org/10.1093/biomet/70.1.41.
- Rubin, D. B. (1973), “The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies,” Biometrics, 29, 185–203. DOI: https://doi.org/10.2307/2529685.
- Stuart, E. A. (2010), “Matching Methods for Causal Inference: A Review and a Look Forward,” Statistical Science, 25, 1–21. Mathematical Reviews number (MathSciNet): MR2741812. DOI: https://doi.org/10.1214/09-STS313.
- Thomas, J., Avellar, S. A., Deke, J., and Gleason, P. (2017), “Matched Comparison Group Design Standards in Systematic Reviews of Early Childhood Interventions,” Evaluation Review 41, 240–279. DOI: https://doi.org/10.1177/0193841X17708721.
- Zubizarreta, J. R. (2015), “Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data,” Journal of the American Statistical Association, 110, 910–922. DOI: https://doi.org/10.1080/01621459.2015.1023805.
- Zubizarreta, J. R., Paredes, R. D., and Rosenbaum, P. R. (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, 204–231. DOI: https://doi.org/10.1214/13-AOAS713.