Targeted maximum likelihood estimation of natural direct effects

International Journal of Biostatistics

Volumn 8, Issue 1, 2012, Pages

  Zheng, Wenjing ^a   Van Der Laan, Mark J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Asymptotic efficiency; Asymptotic linearity; Canonical gradient; Direct effects; Double robust; Efficient influence curve; Efficient score; Loss based learning; Mediation analysis; Mediation formula; Mediator; Natural direct effects; Natural indirect effects; Parametric working submodels; Robust; Targeted learning; Targeted maximum likelihood estimator

Indexed keywords

ARTICLE; EFFECT SIZE; LOGISTIC REGRESSION ANALYSIS; MATHEMATICAL ANALYSIS; MAXIMUM LIKELIHOOD METHOD; MOLECULAR DYNAMICS; MULTIPLE REGRESSION; OUTCOME ASSESSMENT; PARAMETRIC TEST; PREDICTIVE VALUE; PROCESS MODEL; SAMPLE SIZE; SCORING SYSTEM; SIMULATION; STATISTICAL MODEL; STATISTICAL PARAMETERS; CONCEPTUAL FRAMEWORK; COUNTERFACTUAL FRAMEWORK; EFFICIENT SCORE; ENDOGENOUS VARIABLE; ESTIMATING EQUATION APPROACH; EXOGENOUS VARIABLE; G COMPUTATION APPROACH; MATHEMATICAL MODEL; MATHEMATICAL VARIABLE; MEDIATOR DENSITY; MINUS LOGLIKELIHOOD LOSS FUNCTION; NATURAL DIRECT EFFECT; PROBABILITY; RANDOMIZATION; STATISTICAL BIAS; TARGETED MAXIMUM LIKELIHOOD ESTIMATION; VARIANCE;

BIOSTATISTICS; CAUSALITY; COMPUTER SIMULATION; HUMANS; LIKELIHOOD FUNCTIONS; MATHEMATICAL CONCEPTS; MODELS, STATISTICAL; STATISTICS, NONPARAMETRIC; TREATMENT OUTCOME;

EID: 84857752877     PISSN: None     EISSN: 15574679     Source Type: Journal    
DOI: 10.2202/1557-4679.1361     Document Type: Article

References (38)

1. Baron, R. and D. Kenny (1986): "The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations", Journal of Penalty and Social Psychology, 51, 1173-1182.
2. Bickel, P., C. Klaassen, Y. Ritov, and J. Wellner (1997): Efficient and Adaptive Estimation for Semiparametric Models, Springer-Verlag.
3. Gruber, S. and M. van der Laan (2010): "A targeted maximum likelihood estimator of a causal effect on a bounded continuous outcome", International Journal of Biostatistics, 6.
4. Gruber, S. and M. van der Laan (2011): "Bounded continuous outcomes", in M. van der Laan and S. Rose, eds., Targeted Learning: Causal Inference for Observational and Experimental Data, Springer.
5. Hafeman, D. and T. Vander Weele (2010): "Alternative assumptions for the identification of direct and indirect effects", Epidemiology.
6. Holland, P. (1986): "Statistics and causal inference", Journal of the American Statistical Association, 81, 945-960.
7. Imai, K., L. Keele, and T. Yamamoto (2010): "Identification, inference and sensitivity analysis for causal mediation effects", Statistical Science, 25, 51-71.
8. Jo, B., E. Stuart, D. MacKinnon, and A. Vinokur (2011): "The use of propensity scores in mediation analysis", Multivariate Behavioral Research, 46, 425-452.
9. Kang, J. and J. Schafer (2007): "Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data (with discussion)", Statistical Science, 22, 523-39.
10. Kaufman, J., R. Maclehose, and S. Kaufman (2004): "A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. " Epidemiologic Perspectives & Innovations, 1:4.
11. Kosorok, M. (2008): Introduction to Empirical Processes and Semiparametric Inference, Springer-Verlag.
12. Pearl, J. (2001): "Direct and indirect effects", in Proceedings of the seventeenth conference on uncertainty in artificial intelligence, Citeseer, 411-420.
13. Pearl, J. (2009): Causality: Models, Reasoning and Inference, New York: Cambridge University Press, 2nd edition.
14. Pearl, J. (2011): "The mediation formula: A guide to the assessment of causal pathways in nonlinear models", in C. Berzuini, P. Dawid, and L. Bernardinelli, eds., Causality: Statistical Perspectives and Applications.
15. Petersen, M., K. Porter, S. Gruber, Y. Wang, and M. van der Laan (2010): "Diagnosing and responding to violations in the positivity assumption", Technical report 269, Division of Biostatistics, University of California, Berkeley, URL
16. Petersen, M., S. Sinisi, and M. van der Laan (2006): "Estimation of direct causal effects." Epidemiology, 17, 276-284.
17. Robins, J. (1999): "Marginal structural models versus structural nested models as tools for causal inference", in Statistical models in epidemiology: the environment and clinical trials, Springer-Verlag, 95-134.
18. Robins, J. (2003): "Semantics of causal dag models and the identification of direct and indirect effects", in N. H. P. Green and S. Richardson, eds., Highly Structured Stochastic Systems, Oxford University Press, Oxford, 70-81.
19. Robins, J. and S. Greenland (1992): "Identifiability and exchangeability for direct and indirect effects", Epidemiology, 3, 143-155.
20. Robins, J. and A. Rotnitzky (2001): "Comment on the Bickel and Kwon article, "Inference for semiparametric models: Some questions and an answer"", Statistica Sinica, 11, 920-936.
21. Robins, J. M. and T. S. Richardson (2010): "Alternative graphical causal models and the identification of direct effects." in P. Shrout, ed., In Causality and Psychopathology: Finding the Determinants of Disorders and Their Cures, Oxford University Press.
22. Rosenbaum, P. and D. B. Rubin (1983): "The central role of the propensity score in observational studies for causal effects", Biometrika, 70, 41-55.
23. Rubin, D. (1978): "Bayesian inference for causal effects: the role of randomization", Annals of Statistics, 6, 34-58.
24. Tchetgen Tchetgen, E. and I. Shpitser (2011a): "Semiparametric estimation of models for natural direct and indirect effects", Technical report 129, Biostatistics, Harvard University, URL
25. Tchetgen Tchetgen, E. and I. Shpitser (2011b): "Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness, and sensitivity analysis", Technical report 130, Biostatistics, Harvard University, URL
26. Tsiatis, A. (2006): Semiparametric Theory and Missing Data, New York: Springer.
27. van der Laan, M. and S. Gruber (2010): "Collaborative double robust penalized targeted maximum likelihood estimation", The International Journal of Biostatistics, 6.
28. van der Laan, M. and M. Petersen (2004): "Estimation of direct and indirect causal effects in longitudinal studies", Technical report 155, Division of Biostatistics, University of California, Berkeley.
29. van der Laan, M. and J. Robins (2003): Unified methods for censored longitudinal data and causality, Springer, New York.
30. van der Laan, M. and S. Rose (2011): Targeted Learning: Causal Inference for Observational and Experimental Data, Springer Series in Statistics, Springer, first edition.
31. van der Laan, M. and D. Rubin (2006): "Targeted maximum likelihood learning", The International Journal of Biostatistics, 2.
32. van der Laan, M. J. and M. Petersen (2008): "Direct effect models", The International Journal of Biostatistics, 4.
33. van der Vaart, A. (1998): Asymptotic Statistics, Cambridge University Press.
34. Vander Weele, T. (2009): "Marginal structural models for the estimation of direct and indirect effects", Epidemiology, 20, 18-26.
35. Vander Weele, T. and S. Vansteelandt (2010): "Odds ratios for mediation analysis for a dichotomous outcome", Am. J. of Epidemiology, 172, 1339-1348.
36. Vansteelandt, S. (2009): "Estimating direct effects in cohort and case control studies", Epidemiology, 20, 851-860.
37. Zheng, W. and M. van der Laan (2010): "Asymptotic theory for crossvalidated targeted maximum likelihood estimation", Technic alreport 273, Division of Biostatistics, University of California, Berkeley, URL
38. Zheng, W. and M. van der Laan (2011): "Cross-validated targeted minimum-lossbased estimation", in M. van der Laan and S. Rose, eds., Targeted Learning: Causal Inference for Observational and Experimental Data, Springer.