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Recovering from Selection Bias in Causal and Statistical Inference

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  1. Acid, S., and de Campos, L. 1996. An algorithm for finding minimum d-separating sets in belief networks. In Proceedings of the 12th Annual Conference on Uncertainty in Artificial Intelligence, 3–10. San Francisco, CA: Morgan Kaufmann.Google ScholarGoogle Scholar
  2. Angrist, J. D. 1997. Conditional independence in sample selection models. Economics Letters 54(2):103–112.Google ScholarGoogle ScholarCross RefCross Ref
  3. Bareinboim, E., and Pearl, J. 2012. Controlling selection bias in causal inference. In Girolami, M., and Lawrence, N., eds., Proceedings of The Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012), 100–108. JMLR (22).Google ScholarGoogle Scholar
  4. Bareinboim, E., and Pearl, J. 2013a. Meta-transportability of causal effects: A formal approach. In Proceedings of The Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2013), 135–143. JMLR (31).Google ScholarGoogle Scholar
  5. Bareinboim, E., and Pearl, J. 2013b. Causal transportability with limited experiments. In desJardins, M., and Littman, M. L., eds., Proceedings of The Twenty-Seventh Conference on Artificial Intelligence (AAAI 2013), 95–101.Google ScholarGoogle Scholar
  6. Bareinboim, E.; Tian, J.; and Pearl, J. 2014. Recovering from selection bias in causal and statistical inference. Technical Report R-425, Cognitive Systems Laboratory, Department of Computer Science, UCLA. Also in Carla E. Brodley and Peter Stone (Eds.) Proceedings of the Twenty-eighth AAAI Conference on Artificial Intelligence, Palo Alto, CA: AAAI Press, 2410–2416, 2014, “Best Paper Award.”Google ScholarGoogle Scholar
  7. Cooper, G. 1995. Causal discovery from data in the presence of selection bias. Artificial Intelligence and Statistics 140–150.Google ScholarGoogle Scholar
  8. 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.Google ScholarGoogle Scholar
  9. Cortes, C.; Mohri, M.; Riley, M.; and Rostamizadeh, A. 2008. Sample selection bias correction theory. In Proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT ’08, 38–53. Berlin, Heidelberg: Springer-Verlag.Google ScholarGoogle Scholar
  10. Didelez, V.; Kreiner, S.; and Keiding, N. 2010. Graphical models for inference under outcome-dependent sampling. Statistical Science 25(3):368–387.Google ScholarGoogle ScholarCross RefCross Ref
  11. Elkan, C. 2001. The foundations of cost-sensitive learning. In Proceedings of the 17th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI’01, 973–978. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.Google ScholarGoogle Scholar
  12. Geng, Z. 1992. Collapsibility of relative risk in contingency tables with a response variable. Journal Royal Statistical Society 54(2):585–593.Google ScholarGoogle Scholar
  13. Glymour, M., and Greenland, S. 2008. Causal diagrams. In Rothman, K.; Greenland, S.; and Lash, T., eds., Modern Epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins, 3rd edition. 183–209.Google ScholarGoogle Scholar
  14. Greenland, S., and Pearl, J. 2011. Adjustments and their consequences – collapsibility analysis using graphical models. International Statistical Review 79(3):401–426.Google ScholarGoogle ScholarCross RefCross Ref
  15. Heckman, J. 1979. Sample selection bias as a specification error. Econometrica 47:153–161.Google ScholarGoogle ScholarCross RefCross Ref
  16. Hein, M. 2009. Binary classification under sample selection bias. In Candela, J.; Sugiyama, M.; Schwaighofer, A.; and Lawrence, N., eds., Dataset Shift in Machine Learning. Cambridge, MA: MIT Press. 41–64.Google ScholarGoogle Scholar
  17. Jewell, N. P. 1991. Some surprising results about covariate adjustment in logistic regression models. International Statistical Review 59(2):227–240.Google ScholarGoogle ScholarCross RefCross Ref
  18. Koller, D., and Friedman, N. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press.Google ScholarGoogle Scholar
  19. Kuroki, M., and Cai, Z. 2006. On recovering a population covariance matrix in the presence of selection bias. Biometrika 93(3):601–611.Google ScholarGoogle ScholarCross RefCross Ref
  20. Little, R. J. A., and Rubin, D. B. 1986. Statistical Analysis with Missing Data. New York, NY, USA: John Wiley & Sons, Inc.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Mefford, J., and Witte, J. S. 2012. The covariate’s dilemma. PLoS Genet 8(11):e1003096.Google ScholarGoogle ScholarCross RefCross Ref
  22. Pearl, J., and Paz, A. 2013. Confounding equivalence in causal equivalence. In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2010), 433–441. Corvallis, OR: AUAI. Also: Technical Report R-343w, Cognitive Systems Laboratory, Department of Computer Science, UCLA.Google ScholarGoogle Scholar
  23. Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems. San Mateo, CA: Morgan Kaufmann.Google ScholarGoogle Scholar
  24. Pearl, J. 1993. Aspects of graphical models connected with causality. In Proceedings of the 49th Session of the International Statistical Institute, 391–401.Google ScholarGoogle Scholar
  25. Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82(4):669–710.Google ScholarGoogle ScholarCross RefCross Ref
  26. Pearl, J. 2000. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press. Second ed., 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Pearl, J. 2013. Linear models: A useful “microscope” for causal analysis. Journal of Causal Inference 1:155–170.Google ScholarGoogle ScholarCross RefCross Ref
  28. Pirinen, M.; Donnelly, P.; and Spencer, C. 2012. Including known covariates can reduce power to detect genetic effects in case-control studies. Nature Genetics 44:848–851.Google ScholarGoogle ScholarCross RefCross Ref
  29. Robins, J. 2001. Data, design, and background knowledge in etiologic inference. Epidemiology 12(3):313–320.Google ScholarGoogle ScholarCross RefCross Ref
  30. 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, 657–666. New York, NY, USA: ACM.Google ScholarGoogle Scholar
  31. Spirtes, P.; Glymour, C.; and Scheines, R. 2000. Causation, Prediction, and Search. Cambridge, MA: MIT Press, 2nd edition.Google ScholarGoogle Scholar
  32. Storkey, A. 2009. When training and test sets are different: characterising learning transfer. In Candela, J.; Sugiyama, M.; Schwaighofer, A.; and Lawrence, N., eds., Dataset Shift in Machine Learning. Cambridge, MA: MIT Press. 3–28.Google ScholarGoogle Scholar
  33. Textor, J., and Liskiewicz, M. 2011. Adjustment criteria in causal diagrams: An algorithmic perspective. In Pfeffer, A., and Cozman, F., eds., Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI 2011), 681–688. AUAI Press.Google ScholarGoogle Scholar
  34. Tian, J.; Paz, A.; and Pearl, J. 1998. Finding minimal separating sets. Technical Report R-254, University of California, Los Angeles, CA.Google ScholarGoogle Scholar
  35. Whittemore, A. 1978. Collapsibility of multidimensional contingency tables. Journal of the Royal Statistical Society, Series B 40(3):328–340.Google ScholarGoogle Scholar
  36. Zadrozny, B. 2004. Learning and evaluating classifiers under sample selection bias. In Proceedings of the Twenty-first International Conference on Machine Learning, ICML ’04, 114–. New York, NY, USA: ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Zhang, J. 2008. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence 172:1873–1896.Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Books
            Probabilistic and Causal Inference: The Works of Judea Pearl
            February 2022
            946 pages
            ISBN:9781450395861
            DOI:10.1145/3501714

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 4 March 2022

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