Dealing with limited overlap in estimation of average treatment effects

Biometrika

Volumn 96, Issue 1, 2009, Pages 187-199

  Crump, Richard K ^a   Hotz, V Joseph ^b   Imbens, Guido W ^c   Mitnik, Oscar A ^d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b DUKE UNIVERSITY   (United States)

^c HARVARD UNIVERSITY   (United States)

^d UNIVERSITY OF MIAMI   (United States)

Author keywords

Average treatment effect; Causality; Ignorable treatment assignment; Overlap; Propensity score; Treatment effect heterogeneity; Unconfoundedness

Indexed keywords

EID: 60449086859     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asn055     Document Type: Article

References (17)

1. ABADIE, A. & IMBENS, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica 74, 235-67.
2. CONNORS, A. F., SPEROFF T., DAWSON, N. V., THOMAS, C., HARRELL, F. E., WAGNER, D., DESBIENS, N., GOLDMAN, L., WU, A. W., CALIFF, R. M., FULKERSON, W. J., VIDAILLET, H., BROSTE, S., BELLAMY, P., LYNN, J. & KNAUS, W. A. (1996). The effectiveness of right heart catheterization in the initial care of critically ill patients. J. Am. Med. Assoc. 276, 889-97.
3. GRZYBOWSKI, M., CLEMENTS, E. A., PARSONS, L., WELCH, R., TINTINALLI, A. T. & ROSS, M. A. (2003). Mortality benefit of immediate revascularization of acute ST-segment elevation myocardial infarction in patients with contraindications to thrombolytic therapy: A propensity analysis. J. Am. Med. Assoc. 290, 1891-8.
4. HAHN, J. (1998). On the role of the propensity score in efficient semiparametric estimation of average treatment effects. Econométrica 66, 315-31.
5. HECKMAN, J., ICHIMURA, H. & TODD, P. (1998). Matching as an econometric evaluation estimator. Rev. Econ. Studies 65, 261-94.
6. HIRANO, K. & IMBENS, G. W. (2001). Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization. Health Serv. Out. Res. Meth. 2, 259-78.
7. HIRANO, K., IMBENS, G. & RIDDER, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71, 1161-89.
8. HORVITZ, D. & THOMPSON, D. (1952). A generalization of sampling without replacement from a finite universe. J. Am. Statist. Assoc. 46, 663-85.
9. IMBENS, G. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Rev. Econ. Statist. 86, 1-29.
10. MURPHY, D. J. & CLUFF, L. E. (1990). SUPPORT: Study to understand prognoses and preferences for outcomes and risks of treatments. J. Clin. Epidemiol. 43, S1-S123.
11. ROBINS, J. M. & ROTNITZKY, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. J. Am. Statist. Assoc. 90, 122-9.
12. ROSENBAUM, P. (1989). Optimal matching in observational studies. J. Am. Statist. Assoc. 84, 1024-32.
13. ROSENBAUM, P. (2001). Observational Studies, 2nd ed. New York: Springer.
14. ROSENBAUM, P. & RUBIN, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.
15. RUBIN, D. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. J. Educ. Psychol. 66, 688-701.
16. RUBIN, D. (1997). Estimating causal effects from large data sets using propensity scores. Ann. Internal Med. 127, 757-63.
17. VINCENT, J. L., BARON, J., REINHART, K., GATTINONI, L., THUS, L., WEBB, A., MEIER-HELLMANN, A., NOLLET, G. & PERES-BOTA, D. (2002). Anemia and blood transfusion in critically ill patients. J. Am. Med. Assoc. 288, 1499-507.