A distributional approach for causal inference using propensity scores

Journal of the American Statistical Association

Volumn 101, Issue 476, 2006, Pages 1619-1637

  Tan, Zhiqiang ^a  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Causal inference; Control variate; Nonparametric likelihood; Observational study; Propensity score; Sensitivity analysis

Indexed keywords

EID: 33846066470     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/016214506000000023     Document Type: Article

References (46)

1. Brumback, B. A., Heman, M. A., Haneuse, S. J. P. A., and Robins, J. M. (2004), "Sensitivity Analysis for Unmeasured Confounding Assuming a Marginal Structural Model for Repeated Measures," Statistics in Medicine, 23, 749-767.
2. Connors, A. F., Speroff, T., Dawson, N. V., et al. (1996), "The Effectiveness of Right Heart Catheterization in the Initial Care of Critically III Patients," Journal of the American Medical Association, 276, 889-897.
3. Cornfield, J., Haenszel, W., Hammond, E. C., Liliefeld, A., Shimkin, M., and Wynder, E. L. (1959), "Smoking and Lung Cancer: Recent Evidence and a Discussion of Some Questions," Journal of the National Cancer Institute, 22, 173-203.
4. Cox, D. R. (1958), Planning of Experiments, New York: Wiley.
5. Draper, N. R., and Smith, H. (1981), Applied Regression Analysis (2nd ed.), New York: Wiley.
6. Hahn, J. (1998), "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, 66, 315-331.
7. Hammersley, J. M., and Handscomb, D. C. (1964), Monte Carlo Methods, London: Methuen.
8. Hirano, K., and Imbens, G. W. (2002), "Estimation of Causal Effects Using Propensity Score Weighting: An Application of Data on Right Heart Catheterization," Health Services and Outcomes Research Methodology, 2, 259-278.
9. Hirano, K., Imbens, G. W., and Ridder, G. (2003), "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Scores," Econometrica, 71, 1161-1189.
10. Holland, P. W. (1986), "Statistics and Causal Inference" (with discussion). Journal of the American Statistical Association, 81, 945-970.
11. Holland, P. W., and Rubin, D. B. (1988), "Causal Inference in Retrospective Studies," Evaluation Review, 12, 203-231.
12. Imbens, G. W. (2003), "Sensitivity to Exogeneity Assumptions in Program Evaluation," American Economic Review, 93, 126-132.
13. _ (2004), "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," Review of Economics and Statistics, 86, 4-29.
14. Kong, A., McCullagh, P., Meng, X.-L., Nicolae, D., and Tan, Z. (2003), "A Theory of Statistical Models for Monte Carlo Integration" (with discussion), Journal of the Royal Statistical Society, Ser. B, 65, 585-618.
15. Lin, D. Y., Psaty, B. M., and Kronmal, R. A. (1998), "Assessing the Sensitivity of Regression Results to Unmeasured Confounders in Observational Studies," Biometrics, 54, 948-963.
16. Neyman, J. (1923), "On the Application of Probability Theory to Agricultural Experiments: Essay on Principles, Section 9," translated in Statistical Science, 5, 465-480.
17. Niemiro, W. (1992), "Asymptotics for M-Estimators Defined by Convex Minimization," The Annals of Statistics, 20, 1514-1533.
18. Robins, J. M. (1999), "Association, Causation, and Marginal Structural Models," Synthese, 121, 151-179.
19. _ (2002a), Comment on "Using Inverse Weighting and Predictive Inference to Estimate the Effects of Time-Varying Treatments on the Discrete-Time Hazard," by R. Dawson and P. W. Lavori, Statistics in Medicine, 21, 1663-1680.
20. _ (2002b), Comment on "Covariance Adjustment in Randomized Experiments and Observational Studies," by P. R. Rosenbaum, Statistical Science, 17, 309-321.
21. Robins, J. M., and Ritov, Y. (1997), "Toward a Curse of Dimensionality Appropriate (CODA) Asymptotic Theory for Semiparametric Models," Statistics in Medicine, 16, 285-319.
22. Robins, J. M., and Rotnitzky, A. (1995), "Semiparametric Efficiency in Multivariate Regression Models With Missing Data," Journal of the American Statistical Association, 90, 122-129.
23. _ (2001), Comment on "Inference for Semiparametric Models: Some Questions and an Answer," by P. J. Bickel and J. Kwon, Statistica Sinica, 11, 920-936.
24. Robins, J. M., Rotnitzky, A., and Bonetti, M. (2001), Comment on "Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self-Selected Right Censoring," by C. E. Frangakis and D. B. Rubin, Biometrics, 57, 343-347.
25. Robins, J. M., Rotnitzky, A., and Scharfstein, D. O. (1999), "Sensitivity Analysis for Selection Bias and Unmeasured Confounding in Missing Data and Causal Inference Models," in Statistical Models in Epidemiology, eds. E. M. Halloran and D. Berry, New York: Springer-Verlag, pp. 1-92.
26. Robins, J. M., Rotnitzky, A., and van der Laan, M. (2000), Comment on "On Profile Likelihood," by S. A. Murphy and A. W. van der Vaart, Journal of the American Statistical Association, 95, 431-435.
27. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994), "Estimation of Regression Coefficients When Some Regressors Are Not Always Observed," Journal of the American Statistical Association, 89, 846-866.
28. _ (1995), "Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data," Journal of the American Statistical Association, 90, 106-121.
29. Rosenbaum, P. R. (1986), "Dropping Out of High School in the United States: An Observational Study," Journal of Educational Statistics, 11, 207-224.
30. _ (2002a), Observational Studies (2nd ed.), New York: Springer-Verlag.
31. _ (2002b), "Covariance Adjustment in Randomized Experiments and Observational Studies" (with discussion), Statistical Science, 17, 286-327.
32. Rosenbaum, P. R., and Rubin, D. B. (1983a), "The Central Role of the Propensity Score in Observational Studies for Causal Effects," Biometrika, 70, 41-55.
33. _ (1983b), "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.
34. _ (1984), "Reducing Bias in Observational Studies Using Subclassification on the Propensity Score," Journal of the American Statistical Association, 79, 516-524.
35. Rotnitzky, A., and Robins, J. M. (1995), "Semiparametric Regression Estimation in the Presence of Dependent Censoring," Biometrika, 82, 805-820.
36. Rotnitzky, A., Robins, J. M., and Scharfstein, D. O. (1998), "Semiparametric Regression for Repeated Outcomes With Nonignorable Nonresponse," Journal of the American Statistical Association, 93, 1321-1339.
37. Rotnitzky, A., Scharfstein, D. O., Su, T.-L., and Robins, J. M. (2001), "Methods for Conducting Sensitivity Analysis of Trials With Potentially Nonignorable Competing Causes of Censoring," Biometrics, 57, 103-113.
38. Rubin, D. B. (1973), "The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies," Biometrics, 29, 185-203.
39. _ (1974), "Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies," Journal of Educational Psychology, 66, 688-701.
40. _ (1977), "Assignment of Treatment Group on the Basis of a Covariate," Journal of Educational Statistics, 2, 1-26.
41. _ (1978), "Bayesian Inference for Causal Effects: The Role of Randomization," The Annals of Statistics, 7, 34-58.
42. _ (1980), Comment on "Randomization Analysis of Experimental Data: The Fisher Randomization Test," by D. Basu, Journal of the American Statistical Association, 75, 591-593.
43. Scharfstein, D. O., Rotnitzky, A., and Robins, J. M. (1999), "Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models" (with discussion), Journal of the American Statistical Association, 94, 1096-1146.
44. Tan, Z. (2004), "On a Likelihood Approach for Monte Carlo Integration," Journal of the American Statistical Association, 99, 1027-1036.
45. van der Laan, M. J., and Robins, J. M. (2003), Unified Methods for Censored Longitudinal Data and Causality, New York: Springer-Verlag.
46. White, H. (1982), "Maximum Likelihood Estimation of Misspecified Models," Econometrica, 50, 1-25.