Likelihood-based analysis of causal effects of job-training programs using principal stratification

Journal of the American Statistical Association

Volumn 104, Issue 485, 2009, Pages 166-176

  Zhang, Junni L ^a   Rubin, Donald B ^b   Mealli, Fabrizia ^c  

^a Peking University   (China)

^b HARVARD UNIVERSITY   (United States)

^c UNIVERSITY OF FLORENCE   (Italy)

Author keywords

Monotonicity assumption; Principal stratification; Rubin causal model; Treatment effect on wages

Indexed keywords

EID: 70350304288     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/jasa.2009.0012     Document Type: Article

References (54)

1. Abadie, A., Angrist, J., and Imbens, G. (2002), "Instrumental Variables Estimates of the Effects of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, 70, 91-117.
2. Ahn, H., and Powell, J. (1993), "Semiparametric Estimation of Censored Selection Models with a Nonparametric Selection Mechanism," Journal of Econometrics, 58, 3-29.
3. Aitkin, M., and Rubin, D. B. (1985), "Estimation and Hypothesis Testing in Finite Mixture Models," Journal of the Royal Statistical Society, Ser. B, 47, 67-75.
4. Angrist, J., Imbens, G., and Rubin, D. B. (1996), "Identification of Causal Effects Using Instrumental Variables," Journal of the American Statistical Association, 91, 444-455. (Pubitemid 126412813)
5. Barnard, G. A. (1965), "The Use of the Likelihood Function in Statistical Practice," Proceedings of the Fifth Berkeley Symposium, 1, 27-40.
6. Barnard, G. A., Jenkins, G. M., and Winsten, C. B. (1962), "Likelihood Inference and Time Series," Journal of the Royal Statistical Society, Ser. A, 125, 321-372.
7. Barnard, J., Frangakis, C., Hill, J., and Rubin, D. (2003), "A Principal Stratification Approach to Broken Randomized Experiments: A Case Study of School Choice Vouchers in New York City," Journal of the American Statistical Association, 98, 299-323 (with discussion).
8. Basu, D. (1971), "An Essay on the Logical Foundations of Survey Sampling, Part One," in Foundations of Statistical Inference, eds. V.P. Godambe, and D.A. Sprott, Toronto: Holt, Rinehart and Winston.
9. Boyles, R. A. (2008), "The Role of Likelihood in Interval Estimation," The American Statistician, 62, 22-26.
10. Burghardt, J.,McConnell, S., and Schochet, P. (2003), National Job Corps Study: Findings Using Administrative Earnings Records Data. Final Report. Document No. PR03-92., Princeton, NJ: Mathematica Policy Research, Inc.
11. Burghardt, J., McConnell, S., Schochet, P., Johnson, T., Gritz, M., Glazerman, S., Homrighausen, J., and Jackson, R. (2001), Does Job Corps Work? Summary of the National Job Corps Study. Document No. PR01-50., Princeton, NJ: Mathematica Policy Research, Inc.
12. Copas, J. B., and Li, H. G. (1997), "Inference for Non-random Samples (with discussion)," Journal of the Royal Statistical Society, Ser. B, 59, 55 95.
13. Das, M., Newey, W., and Vella, F. (2003), "Nonparametric Estimation of Sample Selection Models," The Review of Economic Studies, 52, 33-58.
14. Dehejia, R., and Wahba, S. (1999), "Causal Effects in Non-experimental Studies: Re-evaluating the Evaluation of Training Programs," Journal of the American Statistical Association, 94, 1053-1062.
15. Dempster, A. P., Laird, N., and Rubin, D. B. (1977), "Maximum Likelihood Estimation From Incomplete Data Using the EM Algorithm (with discussion)," Journal of the Royal Statistical Society, Ser. B, 39, 1-38.
16. Edwards, A. W. F. (1972), Likelihood, Cambridge, UK: Cambridge University Press.
17. Efron, B., and Tibshirani, R. J. (1993), An Introduction to the Bootstrap, New York, NY: Chapman and Hall.
18. Fisher, R. A. (1921), "On the "Probable Error" of a Coefficient of Correlation Deduced From a Small Sample," Metron, 1, 3-32.
19. Frangakis, C. E., and Rubin, D. B. (1999), "Addressing Complications of Intention-to-Treat Analysis in the Combined Presence of All-or-None Treatment-Noncomplianze and Subsequent Missing Outcomes," Biometrika, 86, 365-379.
20. - (2002), "The Defining Role of 'Principal Stratification and Effects' for Comparing Treatments Adjusted for Posttreatment Variables: From Treatment Noncompliance to Surrogate endpoints," Biometrics, 58, 191-199.
21. Hacking, I. (1965), Logic of Statistical Inference, New York: Cambridge University Press.
22. Heckman, J. J. (1974), "Shadow Prices, Market Wages, and Labor Supply," Econometrica, 42, 679-694.
23. - (1979), "Sample Selection Bias as a Specification Error," Econometrica, 47, 153-162.
24. Heckman, J. J., and Hotz, V. J. (1989), "Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training," Journal of the American Statistical Association, 84, 862-880.
25. Heckman, J. J., Ichimura, H., Smith, J., and Todd, P. (1998), "Characterizing Selection Bias Using Experimental Data," Econometrica, 66, 1017-1098.
26. Heckman, J. J., and Robb, R. (1985), "Alternative Methods for Evaluating the Impact of Interventions: An Overview," Journal of Econometrics, 30, 239-267.
27. Heckman, J. J., Smith, J., and Clements, N. (1997), "Making the Most out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," The Review of Economic Studies, 64, 487-535.
28. Holland, P. (1986), "Statistics and Causal Inference," Journal of the American Statistical Association, 81, 945-960.
29. Lalonde, R. J. (1995), "The Promise of Public Sector-Sponsored Training Programs," The Journal of Economic Perspectives, 9, 149-168.
30. Lee, D. S. (2005), "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," NBER Working Paper, 11721.
31. Little, R. J. A. (1985), "A Note About Models for Selectivity Bias," Econometrica, 53, 1469-1474.
32. Liu, C. (2004), "Robit Regression: A Robust Alternative to Logit and Probit," in Missing Data and Bayesian Methods in Practice: Contributions by Donald Rubin's Statistical Family, eds. A. Gelman and X. L. Meng, New York: Wiley.
33. Liu, C., and Rubin, D. B. (1998), "Ellipsoidally Symmetric Extensions of the General Location Model for Mixed Categorical and Continuous Data," Biometrika, 85, 673-688.
34. Manski, C. F. (1995), Identification Problems in the Social Sciences, Cambridge, MA: Harvard University Press.
35. Mattei, A., and Mealli, F. (2007), "Application of the Principal Stratification Approach to the Faenza Randomized Experiment on Breast Self-Examination," Biometrics, 63, 437-446.
36. McLachlan, G., and Peel, D. (2000), Finite Mixture Models, New York: John Wiley and Sons.
37. Neyman, J. (1923), "On the Application of Probability Theory to Agricultural Experiments: Essay on Principles," Translated in Statistical Science, 5, 465-480.
38. Rosenbaum, P. (1984), "The Consequences of Adjustment for a Concomitant Variable That has Been Affected by the Treatment," Journal of the Royal Statistical Society, Ser. A, 147, 656-666.
39. Royall, R. M. (1997), Statistical Evidence, London: Chapman and Hall.
40. Rubin, D. B. (1974), "Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies," Journal of Educational Psychology, 66, 688-701.
41. - (1976), "Inference and Missing Data," Biometrika, 63, 581-592.
42. - (1978), "Bayesian Inference for Causal Effects," The Annals of Statistics, 6, 34-58.
43. - (1980), "Discussion of "Randomization Analysis of Experimental Data: The Fisher Randomization Test" by D. Basu," Journal of the American Statistical Association, 75, 591-593.
44. - (1998), "More Powerful Randomization-Based p-values in Double- Blind Trials with Non-compliance (with Discussion by D. R. Cox)," Statistics in Medicine, 17, 371-389.
45. - (2000), "The Utility of Counterfactuals for Causal Inference-Discussion of "Causal Inference Without Counterfactuals" by A. P. Dawid," Journal of the American Statistical Association, 95, 435-438.
46. - (2005), "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, 100, 322-331.
47. - (2006), "Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with Censoring Due to Death," Statistical Science, 21, 299-321.
48. SAS Institute, Inc. (2004), SAS/Stat 9.1 User's Guide, Cary, NC.
49. Schochet, P. Z., Bellotti, J., Cao, R. J., Glazerman, S., Grady, A., Gritz, M., McConnell, S., Johnson, T., and Burghardt, T. (2003), "National Job Corps Study: Data Documentation and Public Use Files: Volume I," Mathematica Policy Research, Inc.
50. Titterington, D., Smith, A., and Makov, U. (1985), Statistical Analysis of Finite Mixture Distributions, New York: John Wiley and Sons.
51. Vella, F. (1998), "Estimating Models with Sample Selection Bias: A Survey," The Journal of Human Resources, 33, 127-169.
52. Wooldridge, J. (2002), Econometric Analysis of Cross Section and Panel Data, Cambridge, MA: MIT Press.
53. Zhang, J. L., and Rubin, D. B. (2003), "Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by 'Death'," Journal of Educational and Behavioral Statistics, 28, 353-368.
54. Zhang, J. L., Rubin, D. B., and Mealli, F. (2008), "Evaluating The Effects of Job Training Programs onWages through Principal Stratification," in Volume21 of Advances in Econometrics: Modeling and Evaluating Treatment Effects in Econometrics, eds. D. Millimet, J. Smith, and E. Vytlacil, Oxford, UK: JAI Press.