A new criterion for confounder selection

Biometrics

Volumn 67, Issue 4, 2011, Pages 1406-1413

  Vanderweele, Tyler J ^a   Shpitser, Ilya ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Causal inference; Confounding; Covariate selection; Directed acyclic graphs

Indexed keywords

ACYCLIC GRAPHS; CAUSAL DIAGRAM; CAUSAL INFERENCES; CAUSAL RELATIONSHIPS; CONFOUNDER; CONFOUNDING; COVARIATE SELECTION; COVARIATES; DIRECTED ACYCLIC GRAPH; PRE-TREATMENTS;

DIRECTED GRAPHS;

COVARIANCE ANALYSIS; DIAGRAM; GRAPHICAL METHOD; KNOWLEDGE; OBSERVATIONAL METHOD;

EID: 80054726997     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2011.01619.x     Document Type: Article

References (34)

1. Abadie, A. and Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica 74, 235-267.
2. Brenner, H. (1993). Bias due to non-differential misclassification of polytomous confounders. Journal of Clinical Epidemiology 46, 57-63.
3. Cox, D. R. (1958). Planning of Experiments. New York: John Wiley & Sons.
4. Dawid, A. P. (2002). Influence diagrams for causal modelling and inference. International Statistical Review 70, 161-189.
5. Geiger, D., Verma, T. S., and Pearl, J. (1990). Identifying independence in Bayesian networks. Networks 20, 507-534.
6. Glymour, M. M., Weuve, J., and Chen, J. T. (2008). Methodological challenges in causal research on racial and ethnic patterns of cognitive trajectories: Measurement, selection, and bias. Neuropsychology Review 18, 194-213.
7. Hernán, M. A. and Robins, J. M. (2006). Estimating causal effects from epidemiological data. Journal of Epidemiology and Community Health 60, 578-586.
8. Lauritzen, S. (1996). Graphical Models. Oxford, U.K.: Oxford University Press.
9. Lauritzen, S. L., Dawid, A. P., Larsen, B. N., and Leimer, H. G. (1990). Independence properties of directed Markov fields. Networks 20, 491-505.
10. Pearl, J. (1995). Causal diagrams for empirical research. Biometrika 82, 669-688.
11. Pearl, J. (2009). Causality: Models, Reasoning, and Inference. Cambridge, U.K.: Cambridge University Press.
12. 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, P. Grunwald and P. Spirtes (eds), 417-424. Corvallis, Oregon: AUAI.
13. Richardson, T. S. and Spirtes, P. (2002). Ancestral graph Markov models. Annals of Statistics 30, 962-1030.
14. Robins, J. M. (1997). Causal inference from complex longitudinal data. In Latent Variable Modeling and Applications to Causality. Lecture Notes in Statistics (120), M. Berkane (ed.), 69-117. New York: Springer Verlag.
15. Robins, J. M. (2003). Semantics of causal DAG models and the identification of direct and indirect effects. In Highly Structured Stochastic Systems, P. Green, N. L. Hjort, and S. Richardson (eds), 70-81. New York: Oxford University Press.
16. Robinson, L. and Jewell, N. P. (1991). Some surprising results about covariate adjustment in logistic regression models. International Statistical Review 59, 227-240.
17. 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.
18. Rosenbaum, P. R. and Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistical Association 39, 33-38.
19. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688-701.
20. Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics 6, 34-58.
21. Rubin, D. B. (1990). Formal modes of statistical inference for causal effects. Journal of Statistical Planning and Inference 25, 279-292.
22. Rubin, D. B. (2007). The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials. Statistics in Medicine 26, 20-36.
23. Rubin, D. B. (2008). Author's reply (to Ian Shrier's letter to the editor). Statistics in Medicine 27, 2741-2742.
24. Rubin, D. B. (2009). Author's reply (to Judea Pearl's and Arvid Sjölander's letters to the editor). Statistics in Medicine 28, 1420-1423.
25. Schisterman, E. F., Cole, S. R., and Platt, R. W. (2009). Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology 20, 488-495.
26. Sekhon, J. S. (2011). Matching: Multivariate and propensity score matching with balance optimization. Journal of Statistical Software 42, 1-52.
27. Shpitser, I. and Pearl, J. (2008). Dormant independence. In Proceedings of the Twenty-Fourth Conference on Artificial Intelligence 24, 1081-1087.
28. Shpitser, I., VanderWeele, T. J., and Robins, J. M. (2010). On the validity of covariate adjustment for estimating causal effects. Proceedings of the 26th Conference on Uncertainty and Artificial Intelligence, 527-536. Corvallis, Washington: AUAI.
29. Shrier, I. (2008). Letter to the editor. Statistics in Medicine 27, 2740-2741.
30. Sjölander, A. (2009). Letter to the editor. Statistics in Medicine 28, 1416-1420.
31. Spirtes, P., Glymour, C., and Scheines, R. (1993). Causation, Prediction and Search. New York: Springer-Verlag.
32. Subramanian, S. V., Glymour, M. M., and Kawachi, I. (2007). Identifying causal ecologic effects on health: A methodological assessment. In Macrosocial Determinants of Health, S. Galea (ed.), Chapter 15, 301-331. New York: Springer Media.
33. Verma, T. and Pearl, J. (1988). Causal networks: Semantics and expressiveness. In Proceedings of the Fourth Workshop on Uncertainty in Artificial Intelligence, 352-359. Reprinted in Uncertainty in Artificial Intelligence, R. Shachter, T. S. Levitt, and L. N. Kanal (eds), 4, 69-76. Amsterdam: Elsevier.
34. Wooldridge, J. (2009). Should instrumental variables be used as matching variables? Michigan State University Technical Report, available at.