Discussions

Biometrics

Volumn 68, Issue 3, 2012, Pages 675-678

  Vansteelandt, Stijn ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

NUMERICAL MODEL; THEORETICAL STUDY;

EID: 84866774377     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2011.01734.x     Document Type: Article

References (11)

1. Budtz-Jorgensen, E., Keiding, N., Grandjean, P., and Weihe, P. (2007). Confounder selection in environmental epidemiology: Assessment of health effects of prenatal mercury exposure. Annals of Epidemiology 17, 27-35.
2. Crainiceanu, C.M., Dominici, F., and Parmigiani, G. (2008). Adjustment uncertainty in effect estimation. Biometrika 95, 635-651.
3. Greenland, S. (2008). Variable selection versus shrinkage in the control of multiple confounders. American Journal of Epidemiology 167, 523-529.
4. Hahn, J. (2004). Functional restriction and efficiency in causal inference. Review of Economics Statitics 86, 73-76.
5. McCandless, L.C., Gustafson, P., and Austin, P.C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine 28, 94-112.
6. Pearl, J. (2010). On a class of bias-amplifying covariates that endanger effect estimates. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, Grunwald P and Spirtes P (eds), 417-424, AOAI, Corvallis, OR.
7. Robins, J.M., Mark, S.D., and Newey, W.K. (1992). Estimating exposure effects by modelling the expectation of exposure conditional on confounders. Biometrics 48, 479-495.
8. Robinson, L.D. and Jewell, N.P. (1991). Some surprising results about covariate adjustment in logistic regression models. International Statistical Review 59, 227-240.
9. Rosenbaum, P.R. and Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.
10. Tan, Z. (2006). A distributional approach for causal inference using propensity scores. Journal of the American Statistical Association 101, 1619-1637.
11. Vansteelandt, S., Bekaert, M., and Claeskens, G. (2012). On model selection and model misspecification in causal inference. Statistical Methods in Medical Research 21, 7-30.