CENEVAL INVESTIGA 27
Lecturas recomendadas
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 (25), 3083–3107. https://doi. org/10.1002/sim.3697. Austin, P. C. (2011). 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 . Ho, D. E., Imai, K., King, G., & Stuart, E. A. (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 . Ho, D. (2011). MatchIt: nonparametric preprocessing for parametric causal inference 42. https://doi.org/10.18637/jss.v042.i08 . Rosenbaum, P. R., & Rubin D. B. (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 . Stuart, E. A. (2010). Matching methods for causal Inference: a review and a look forward. Statistical Science, 25 (1). https://doi.org/10.1214/09- sts313 . Stuart, E. A., & Green, K. M. (2008). Using full matching to estimate causal effects in nonexperimental studies: examining the relationship between adolescent marijuana use and adult outcomes. Developmental Psychology, 44 (2), 395–406. https://doi.org/10.1037/0012-1649.44.2.395 .
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