Controlling selection bias in causal inference

Journal of Machine Learning Research

Volumn 22, Issue , 2012, Pages 100-108

  Bareinboim, Elias ^a   Pearl, Judea ^a  

^a Engineering IV   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

ALGEBRAIC METHOD; CAUSAL INFERENCES; INSTRUMENTAL VARIABLES; NONPARAMETRIC METHODS; OBSERVATIONAL STUDY; RANDOMIZED EXPERIMENTS; SELECTION BIAS; STATISTICAL INFERENCE;

ALGEBRA;

EID: 84894404372     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (25)

1. Balke, A. and Pearl, J. (1997). Bounds on treatment effects from studies with imperfect compliance. Journal of the American Statistical Association 92 1172-1176.
2. Cooper, G. (1995). Causal discovery from data in the presence of selection bias. Artificial Intelligence and Statistics 140-150.
3. Cornfield, J. (1951). A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix. Journal of the National Cancer Institute 11 1269-1275.
4. Didelez, V., Kreiner, S. and Keiding, N. (2010). Graphical models for inference under outcome-dependent sampling. Statistical Science 25(3) 368-387.
5. Geneletti, S., Richardson, S. and Best, N. (2009). Adjusting for selection bias in retrospective, case-control studies. Biostatistics 10(1).
6. Geng, Z. (1992). Collapsibility of relative risk in contingency tables with a response variable. Journal Royal Statistical Society 54 585-593.
7. Glymoijr, M. and Greenland, S. (2008). Causal diagrams. In Modern Epidemiology (K. Rothman, S. Greenland and T. Lash, eds.), 3rd ed. Lippincott Williams & Wilkins, Philadelphia, PA, 183-209.
8. Greenland, S. and Pearl, J. (2011). Adjustments and their consequences-collapsibility analysis using graphical models. International Statistical Review 79 401-426.
9. Heckman, J. (1970). Sample selection bias as a specification error. Econometrica 47 153-161.
10. Hein, M. (2009). Binary classification under sample selection bias. In Dataset Shift in Machine Learning (J. Candela, M. Sugiyama, A. Schwaighofer and N. Lawrence, eds.). MIT Press, Cambridge, MA, 41-64.
11. Hernan, M., HernAndez-Diaz, S. and Robins, J. (2004). A structural approach to selection bias. Epidemiology 15 615-625.
12. Horwitz, R. and Feinstein, A. (1978). Alternative analytic methods for case-control studies of estrogens and endometrial cancer. New England Journal of Medicine 299 368-387.
13. Lajjritzen, S. L. and Richardson, T. S. (2008). Discussion of mccullagh: Sampling bias and logistic models. J. Roy. Statist. Soc. Ser. B 70 140-150.
14. Pearl, J. (2009). Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press, New York.
15. Pearl, J. (2010). On a class of bias-amplifying variables that endanger effect estimates. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence. AUAI, Corvallis, OR, 425-432.
16. Pearl, J. and Bareinboim, E. (2011). Transportability of causal and statistical relations: A formal approach. In Proceedings of the Twenty-Fifth National Conference on Artificial Intelligence. AAAI Press, Menlo Park, CA, 247-254.
17. Robins, J. (2001). Data, design, and background knowledge in etiologic inference. Epidemiology 12 313-320.
18. Robins, J. M., Hernan, M. and Brjjmback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology 11 550-560.
19. Smith, A. T. and Elkan, C. (2007). Making generative classifiers robust to selection bias. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. KDD '07, ACM, New York, NY, USA.
20. Storkey, A. (2009). When training and test sets are different: characterising learning transfer. In Dataset Shift in Machine Learning (J. Candela, M. Sugiyama, A. Schwaighofer and N. Lawrence, eds.). MIT Press, Cambridge, MA, 3-28.
21. Tian, J., Paz, A. and Pearl, J. (1998). Finding minimal separating sets. Tech. Rep. R-254, , Computer Science Department, University of California, Los Angeles, CA.
22. Verma, T. and Pearl, J. (1990). Equivalence and synthesis of causal models. In Uncertainty in Artificial Intelligence, Proceedings of the Sixth Conference. Cambridge, MA. Also in P. Bonissone, M. Henrion, L.N. Kanal and J.F. Lemmer (Eds.), Uncertainty in Artificial Intelligence 6, Elsevier Science Publishers, B.V., 255-268, 1991.
23. Whittemore, A. (1978). Collapsibility of multidimensional contingency tables. Journal of the Royal Statistical Society, Series B 40 328-340.
24. Zadrozny, B. (2004). Learning and evaluating classifiers under sample selection bias. In ICML (C. E. Brodley, ed.), vol. 69 of ACM International Conference Proceeding Series. ACM.
25. Zhang, J. (2008). On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artif. Intell. 172 1873-1896.