Causal inference with general treatment regimes: Generalizing the propensity score

Journal of the American Statistical Association

Volumn 99, Issue 467, 2004, Pages 854-866

  Imai, Kosuke ^a   Van Dyk, David A ^b  

^a PRINCETON UNIVERSITY   (United States)

^b Ringgold Standard Institution Informatics   (United States)

Author keywords

Medical expenditure; Nonrandom treatment assignment; Observational studies; Return to schooling; Subclassification; Treatment effect

Indexed keywords

EID: 4944223958     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/016214504000001187     Document Type: Article

References (55)

1. Angrist, J. D., and Imbens, G. W. (1995), "Two-Stage Least Squares Estimation of Average Causal Effects in Models With Variable Treatment Intensity," Journal of the American Statistical Association, 90, 431-442.
2. Angrist, J. D., Imbens, G. W., and Krueger, A. B. (1999), "Jackknife Instrumental Variables Estimation," Journal of Applied Econometrics, 14, 57-67.
3. Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996), "Identification of Causal Effects Using Instrumental Variables" (with discussion), Journal of the American Statistical Association, 91, 444-455.
4. Angrist, J. D., and Krueger, A. B. (1991), "Does Compulsory School Attendance Affect Schooling and Earnings?" Quarterly Journal of Economics, 106, 979-1014.
5. _ (1992), "The Effect of Age at School Entry on Educational Attainment: An Application of Instrumental Variables With Moments From Two Samples," Journal of the American Statistical Association, 87, 328-336.
6. _ (1995), "Split-Sample Instrumental Variables Estimates of the Return to Schooling," Journal of Business & Economic Statistics, 13, 225-235.
7. Bound, J., Jaeger, D. A., and Baker, R. M. (1995), "Problems With Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variable Is Weak," Journal of the American Statistical Association, 90, 443-450.
8. Card, D. E. (1995), "Earnings, Schooling, and Ability Revisited," Research in Labor Economics, 14, 23-48.
9. D'Agostino, R. B., Jr., and Rubin, D. B. (2000), "Estimating and Using Propensity Scores With Partially Missing Data," Journal of the American Statistical Association, 95, 451, 749-759.
10. Dehejia, R. H., and Wahba, S. (1999), "Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs," Journal of the American Statistical Association, 94, 1053-1062.
11. DiNardo, J., and Tobias, J. L. (2001), "Nonparametric Density and Regression Estimation," Journal of Economic Perspectives, 15, 11-28.
12. Drake, C. (1993), "Effects of Misspecification of the Propensity Score on Estimators of Treatment Effect," Biometrics, 49, 1231-1236.
13. Duan, N., Manning, W. G. J., Morris, C. N., and Newhouse, J. P. (1983), "A Comparison of Alternative Models for the Demand for Medical Care," Journal of Business & Economic Statistics, 1, 115-126.
14. Efron, B., and Feldman, D. (1991), "Compliance as an Explanatory Variable in Clinical Trials" (with discussion), Journal of the American Statistical Association, 86, 9-17.
15. Gerber, A. S., and Green, D. P. (2000), "The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout: A Field Experiment," American Political Science Review, 94, 653-663.
16. Hastie, T. J., and Tibshirani, R. (1990), Generalized Additive Models, London: Chapman & Hall.
17. Heckman, J. J., Ichimura, H., and Todd, P. (1998), "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, 65, 261-294.
18. Hill, J., Rubin, D. B., and Thomas, N. (1999), "The Design of the New York School Choice Scholarship Program Evaluation," in Research Designs: Inspired by the Work of Donald Campbell, ed. L. Bickman, Thousand Oaks, CA: Sage, pp. 155-180.
19. Hirano, K., Imbens, G., and Ridder, G. (2003), "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, 71, 1307-1338.
20. Holland, P. W. (1986), "Statistics and Causal Inference" (with discussion), Journal of the American Statistical Association, 81, 945-960.
21. Imai, K. (2004), "Do Get-Out-the-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments," American Political Science Review, to appear.
22. Imai, K., and van Dyk, D. A. (2004), "A Bayesian Analysis of the Multinomial Probit Model Using Marginal Data Augmentation," Journal of Econometrics, to appear.
23. Imbens, G. W. (2000), "The Role of the Propensity Score in Estimating Dose-Response Functions," Biometrika, 87, 706-710.
24. Imbens, G. W., and Rubin, D. B. (1997), "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," Review of Economic Studies, 64, 555-574.
25. Javaras, K. N., and van Dyk, D. A. (2003), "Multiple Imputation for Incomplete Data With Semicontinuous Variables," Journal of the American Statistical Association, 98, 703-715.
26. Joffe, M. M., and Rosenbaum, P. R. (1999), "Propensity Scores," American Journal of Epidemiology, 150, 327-333.
27. Johnson, E., Dominici, F., Griswold, M., and Zeger, S. L. (2003), "Disease Cases and Their Medical Costs Attributable to Smoking: An Analysis of the National Medical Expenditure Survey," Journal of Econometrics, 112, 135-151.
28. Kling, J. R. (2001), "Interpreting Instrumental Variables Estimates of the Returns to Schooling," Journal of Business & Economic Statistics, 19, 358-364.
29. Larsen, M. D. (1999), "An Analysis of Survey Data on Smoking Using Propensity Scores," Sankhyā, Ser. B, 61, 91-105.
30. Lechner, M. (1999), "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany After Unification," Journal of Business & Economic Statistics, 17, 74-90.
31. Li, Q., Huang, C. J., Li, D., and Fu, T.-T. (2002), "Semiparametric Smooth Coefficient Models," Journal of Business & Economic Statistics, 20, 412-422.
32. Lu, B., Zanutto, E., Hornik, R., and Rosenbaum, P. R. (2001), "Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse," Journal of the American Statistical Association, 96, 1245-1253.
33. McCullagh, P., and Nelder, J. A. (1989), Generalized Linear Models (2nd ed.), London: Chapman & Hall.
34. Olsen, M. K., and Schafer, J. L. (2001), "A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data," Journal of the American Statistical Association, 96, 730-745.
35. Robins, J. M., and Rotnitzky, A. (2001), Comment on "Inference for Semipara-metric Models: Some Questions and an Answer," by J. P. Bickel and J. Kwon, Statistica Sinica, 11, 920-936.
36. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1995), "Analysis of Semipara-metric Regression Models for Repeated Outcomes in the Presence of Missing Data," Journal of the American Statistical Association, 90, 106-121.
37. Rosenbaum, P. R. (1987), "Model-Based Direct Adjustment," Journal of the American Statistical Association, 82, 387-394.
38. Rosenbaum, P. R., and Rubin, D. B. (1983a), "Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study With Binary Outcome," Journal of the Royal Statistical Society, Ser B, 45, 212-218.
39. _ (1983b), "The Central Role of the Propensity Score in Observational Studies for Causal Effects," Biometrika, 70, 41-55.
40. _ (1984), "Reducing Bias in Observational Studies Using Subclassification on the Propensity Score," Journal of the American Statistical Association, 79, 516-524.
41. _ (1985), "Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score," The American Statistician, 39, 33-38.
42. Rouse, C. E. (1995), "Democratization or Diversion? The Effect of Community Colleges on Educational Attainment," Journal of Business & Economic Statistics, 13, 217-224.
43. Rubin, D. B. (1973), "Matching to Remove Bias in Observational Studies," Biometrics, 29, 159-183.
44. _ (1979), "Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies," Journal of the American Statistical Association, 74, 318-328.
45. _ (1980), Comments on "Randomization Analysis of Experimental Data: The Fisher Randomization Test," by D. Basu, Journal of the American Statistical Association, 75, 591-593.
46. _ (1990), Comments on "On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9," by J. Splawa-Neyman, translated from the Polish and edited by D. M. Dabrowska and T. P. Speed, Statistical Science, 5, 472-480.
47. _ (2000), "Statistical Issues in the Estimation of the Causal Effects of Smoking Due to the Conduct of the Tobacco Industry," in Statistical Science in the Courtroom, ed. J. L. Gastwirth, New York: Springer-Verlag, pp. 321-351.
48. _ (2001), "Estimating the Causal Effect of Smoking," Statistics in Medicine, 20, 1395-1414.
49. Rubin, D. B., and Thomas, N. (1992), "Affinely Invariant Matching Methods With Ellipsoidal Distributions," The Annals of Statistics, 20, 1079-1093.
50. _ (1996), "Matching Using Estimated Propensity Scores: Relating Theory to Practice," Biometrics, 52, 249-264.
51. _ (2000), "Combining Propensity Score Matching With Additional Adjustments for Prognostic Covariates," Journal of the American Statistical Association, 95, 573-585.
52. Staiger, D., and Stock, J. H. (1997), "Instrumental Variables Regression With Weak Instruments," Econometrica, 65, 557-586.
53. Wood, S. (2003), "Thin Plate Regression Splines," Journal of the Royal Statistical Society, Ser. B, 65, 95-114.
54. Yatchew, A. (1998), "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, 36, 669-721.
55. Zeger, S., Wyant, T., Miller, L., and Samet, J. (2000), "Statistical Testimony on Damages in Minnesota versus the Tobacco Industry" in Statistical Science in the Courtroom, ed. J. L. Gastwirth, New York: Springer-Verlag, pp. 303-320.