Improved double-robust estimation in missing data and causal inference models

Biometrika

Volumn 99, Issue 2, 2012, Pages 439-456

  Rotnitzky, Andrea ^a   Lei, Quanhong ^b   Sued, Mariela ^c   Robins, James M ^d  

^a UNIVERSIDAD TORCUATO DI TELLA   (Argentina)

^b Adheris Inc   (United States)

^c UNIVERSIDAD DE BUENOS AIRES   (Argentina)

^d HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Drop out; Marginal structural model; Missing at random

Indexed keywords

EID: 84861641356     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/ass013     Document Type: Article

References (22)

1. BANG, H. & ROBINS, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61, 692-972.
2. CAO, W., TSIATIS, A. & DAVIDIAN, M. (2009). Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data. Biometrika 96, 723-34.
3. GILL, R. D. (1989). Non- and semi-parametric maximum likelihood estimators and the von Mises method. Scand. J. Statist. 16, 97-128.
4. KANG, D. Y. L. & SCHAFER, J. L. (2007). Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data (with discussion and rejoinder). Statist. Sci. 22, 523-80.
5. ROBINS, J. M. (1999).Marginal structural models versus structural nested models as tools for causal inference. Statistical Models in Epidemiology: The Environment and Clinical Trials, Ed. M. E. Halloran and D. Berry, pp. 95-134, Institute for Mathematics and its Applications 116. New York: Springer.
6. ROBINS, J. M. (2000). Robust estimation in sequentially ignorable missing data and causal inference models. Proc. Am. Statist. Assoc. Sect. Bayesian Statist. Sci., 6-10.
7. ROBINS, J. M. & ROTNITZKY, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. J. Am. Statist. Assoc. 90, 122-9.
8. ROBINS, J. M. & WANG, N. (2000). Inference for imputation estimators. Biometrika 87, 113-24.
9. ROBINS, J. M., ROTNITZKY, A. & ZHAO, L. P. (1994). Estimation of regression-coefficients when some regressors are not always observed. J. Am. Statist. Assoc. 89, 846-66.
10. ROBINS, J. M.,GOMEZ, Q., SUED, M. & ROTNITZKY, A. (2007). Performance of double-robust estimators when inverse probability weights are highly variable. Statist. Sci. 22, 544-59.
11. RUBIN, D. B. (1976). Inference and missing data. Biometrika 63, 581-92.
12. RUBIN, D. & VAN DER LAAN, M. J. (2008). Empirical efficiency maximization: improved locally efficient covariate adjustment in randomized experiments and survival analysis. Int. J. Biostatist. 4, article 5.
13. SCHARFSTEIN, D. O., ROTNITZKY, A. & ROBINS, J. M. (1999). Adjusting for non-ignorable drop-out using semiparametric non-response models. J. Am. Statist. Assoc. 94, 1096-20.
14. TAN, Z. (2006). A distributional approach for causal inference using propensity scores. J. Am. Statist. Assoc. 101, 1619-37.
15. TAN, Z. (2007). Understanding OR, PS and DR. Statist. Sci. 22, 560-8.
16. TAN, Z. (2008). Comment: improved local efficiency and double robustness. Int. J. Biostatist. 4, article 10.
17. TAN, Z. (2010a). Bounded, efficient and doubly robust estimation with inverse weighting. Biometrika 97, 661-82.
18. TAN, Z. (2010b). Nonparametric likelihood and doubly robust estimating equations for marginal and nested structural models. Can. J. Statist. 38, 609-32.
19. VAN DER LAAN, M. J. (2010). Targetedmaximum likelihood based causal inference: part I. Int. J. Biostatist. 6, article 2.
20. VAN DER LAAN, M. J. & ROBINS, J. (2003). Unified Methods for Censored Longitudinal Data and Causality. New York: Springer.
21. VAN DER LAAN, M. J. & RUBIN, D. (2006). Targeted maximum likelihood learning. Int. J. Biostatist. 2, article 11.
22. VAN DER VAART, A. W. (2000). Asymptotic Statistics. Cambridge: Cambridge University Press.