A Primer on Propensity Score Analysis

Newborn and Infant Nursing Reviews

Volumn 10, Issue 1, 2010, Pages 19-26

  Shadish, William R ^a   Steiner, Peter M ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Matching; Nonrandomized experiment; Propensity score; Randomized experiment; Strong ignorability

Indexed keywords

EID: 76549116121     PISSN: 15273369     EISSN: None     Source Type: Journal    
DOI: 10.1053/j.nainr.2009.12.010     Document Type: Article

References (45)

1. Shadish W.R., Cook T.D., and Campbell D.T. Experimental and ouasi-experimental designs for generalized causal inference (2002), Houghton-Mifflin, Boston
2. Cook T.D. "Waiting for life to Arrive": a history of the regression-discontinuity design in psychology, statistics and economics. J Econometrics 142 (2008) 636-654
3. Rosenbaum P.R., and Rubin D.B. The central role of the propensity score in observational studies for causal effects. Biometrika 70 (1983) 41-55
4. Rosenbaum P.R., and Rubin D.B. Reducing bias in observational studies using subclassification on the propensity scores. J Am Stat Assoc 79 (1984) 516-524
5. Shadish W.R., Clark M.H., and Steiner P.M. Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random to nonrandom assignment. J Am Stat Assoc 103 (2008) 1334-1343
6. Steiner PM, Cook TD, Shadish WR, et al. The importance of covariate selection in controlling for selection bias in observational studies. Psychol Methods. In press.
7. Cook TD, Steiner PM. Case Matching and the reduction of selection bias in quasi-experiments: the relative importance of the pretest as a covariate, unreliable measurement and mode of data analysis. Psychological Methods. In press.
8. Steiner PM, Cook TD, Shadish WR. On the importance of reliable covariate measurement in selection bias adjustments using propensity scores. J Educ Behav Stat. In press.
9. Hastie T., Tibshirani R., and Friedman J. The elements of statistical learning (2001), Springer, New York
10. Berk R.A. Statistical learning from a regression perspective (2008), Springer, New York
11. Stone R.A., Obrosky D.S., Singer D.E., et al. Propensity score adjustment for pretreatment differences between hospitalized and ambulatory patients with community-acquired pneumonia. Medical Care 33 (1995) AS56-AS66
12. Berk R.A. An introduction to ensemble methods for data analysis. Sociol Methods Res 34 (2006) 263-295
13. McCaffrey D.F., Ridgeway G., and Morral A.R. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods 9 (2004) 403-425
14. Luellen J.K., Shadish W.R., and Clark M.H. Propensity scores: an introduction and experimental test. Evaluation Review 29 (2005) 530-558
15. Berk R., Li A., and Hickman L.J. Statistical difficulties in determining the role of race in capital cases: a re-analysis of data from the state of Maryland. J Quant Criminol 21 (2005) 365-390
16. R Development Core Team. R: a language and environment for statistical computing (2009), R Foundation for Statistical Computing, Vienna (Austria). 3-900051-07-0. http://www.R-project.org
17. Shadish W.R., Luellen J.K., and Clark M.H. Propensity scores and quasi-experimentation. In: Bootzin R.R., and McKnight P. (Eds). Strengthening research methodology: psychological measurement and evaluation (2006), American Psychological Association, Washington (DC) 143-157
18. Ho D., Imai K., King G., et al. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Anal 15 (2007) 199-236
19. Rubin D.B. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Serv Outcome Res Methodol 2 (2001) 169-188
20. Rubin D.B. For objective causal inference, design trumps analysis. Ann Appl Stat 2 (2008) 808-840
21. Robins J.M., and Rotnitzky A. Semiparametric efficiency in multivariate regression models with missing data. J Am Stat Assoc 90 (1995) 122-129
22. Schafer J.L., and Kang J. Average causal effects from nonrandomized studies: a practical guide and simulated example. Psychol Methods 13 (2008) 279-313
23. Rosenbaum P.R. Observational studies. 2nd ed. (2002), Springer-Verlag, New York
24. Rubin D.B. Matched sampling for causal effects (2006), Cambridge University Press, New York
25. Hansen B.B. Full matching in an observational study of coaching for the SAT. J Am Stat Assoc 99 (2004) 609-619
26. Stuart E.A., and Green K.M. Using full matching to estimate causal effects in non-experimental studies: examining the relationship between adolescent marijuana use and adult outcomes. Dev Psychol 44 (2008) 395-406
27. Cochran W.G. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24 (1968) 295-313
28. Hong G., and Raudenbush S.W. Effects of kindergarten retention policy on children's cognitive growth in reading and mathematics. Educ Eval Policy Anal 27 (2005) 205-224
29. Little R.J.A., and An H. Robust likelihood-based analysis of multivariate data with missing values. Statistica Sinica 14 (2004) 949-968
30. Hirano K., Imbens G., and Ridder G. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71 (2003) 1161-1189
31. Robins J.M., Hernan M.A., and Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 11 (2000) 550-560
32. Cook T.D., Shadish W.R., and Wong V.C. Three conditions under which experiments and observational studies produce comparable causal estimates: new findings from within-study comparisons. J Policy Anal Manag 27 (2008) 724-750
33. Glazerman S., Levy D.M., and Myers D. Nonexperimental versus experimental estimates of earnings impacts. Ann Am Acad 589 (2003) 63-93
34. Shah B.R., Laupacis A., Hux J.E., and Austin P.C. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol 58 (2005) 550-559
35. Stürmer T., Joshi M., Glynn R.J., Avorn J., Rothman K.J., and Schneeweiss S. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J Clin Epidemiol 59 (2006) 437-447
36. Parsons L.S. Reducing bias in a propensity score matched-pair sample using greedy matching techniques. SAS Institute Inc., Proceedings of the Twenty-Sixth Annual SAS Ò Users Group International Conference, Paper 214-26. Cary, NC: SAS Institute Inc. (2001). http://www2.sas.com/proceedings/sugi26/p214-26.pdf
37. Kosanke J., and Bergstralh E. Match cases to controls using variable optimal matching (2004). http://mayoresearch.mayo.edu/mayo/research/biostat/upload/vmatch.sas
38. Levesque R. Raynald's SPSS Tools. http://www.spsstools.net/Syntax/RandomSampling/MatchCasesOnBasisOfPropensityScores.txt
39. Leuven E., and Sianesi B. PSMATCH2. Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical Software Components S432001 (2003), Boston College Department of Economics, Chestnut Hill, Massachusetts
40. Abadie A., Drukker D., Herr J.L., and Imbens G.W. Implementing matching estimators for average treatment effects in Stata. Stata J 4 (2004) 290-311
41. Becker S.O., and Ichino A. Estimation of average treatment effects based on propensity scores. Stata J 2 (2002) 358-377
42. Ho D, Imai K, King G, Stuart EA. MatchIt: nonparametric preprocessing for parametric causal inference. Journal of Statistical Software. In press.
43. Sekhon JS. Multivariate and propensity score matching software with automated balance optimization: The matching package for R. Journal of Statistical Software. In press.
44. Hansen B., and Klopfer S.O. Optimal full matching and related designs via network flows. J Comput Graphical Stat 15 (2006) 609-627
45. Shadish W.R., and Cook T.D. Design rules: more steps towards a complete theory of quasi-experimentation. Stat Sci 14 (1999) 294-300