A step-by-step guide to propensity score matching in R

Practical Assessment, Research and Evaluation

Volumn 19, Issue 18, 2014, Pages 1-6

  Randolph, Justus J ^a   Falbe, Kristina ^a   Manuel, Austin Kureethara ^a   Balloun, Joseph L ^a  

^a MERCER UNIVERSITY   (United States)

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[No Author keywords available]

Indexed keywords

EID: 84930397707     PISSN: None     EISSN: 15317714     Source Type: Journal    
DOI: None     Document Type: Article

References (14)

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2. Falbe, K. (2014). The relationship between Schools to Watch © designation and academic achievement: A study of Colorado, New York, Ohio, and Virginia (Doctoral dissertation). Available from Proquest Dissertations and Theses database. (UMI No. 3581272)
3. Ho, D., Kosuke, I., King, G., & Stuart, E. (2007a). MatchIt: Nonparametric preprocessing for parametric causal inference. Political Analysis, 15(3), 199-236.
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5. Ho, D., Kosuke, I. King, G., & Stuart, E. (2011). MatchIt: Nonparametric preprocessing for parametric causa inference [software documentation]. Retrieved from http://gking.harvard.edu/matchit
6. Ho, D., Kosuke, I., King, G., & Stuart, E. (2013). MatchIt: Nonparametric preprocessing for parametric causal inference [software]. Retrived from http://gking.harvard.edu/matchit
7. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(396), 945-960.
8. Randolph, J. J. (2014a). New York educational data set example before matching. Retrieved from http://justusrandolph.net/psm/newyork.csv
9. Randolph, J. J. (2014b). New York educational data set example after matching. Retrieved from http://justusrandolph.net/psm/newyork_nearest100.c sv
10. R Core Team (2014). R: A language and environment for statistical computing. (3.0.3) [Computer software]. Vienna, Austria: Foundation for Statistical Computing. Retrieved from http://www.R-project.org/.
11. Rubin D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100(496), 322-331.
12. Rudner, L. M., & Peyton, J. (2006). Consider propensity scores to compare treatments. Practical Assessment Research & Evaluation, 11(9). Retrieved from: http://pareonline.net/getvn.asp?v=11&n=9
13. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.
14. Stone, C. A. & Tang, Y. (2013). Comparing propensity score methods in balancing covariates and recovering impact in small sample educational program evaluations. Practical Assessment, Research & Evaluation, 18(13). Retrieved from: http://pareonline.net/getvn.asp?v=18&n=13