European Journal of Epidemiology

, Volume 21, Issue 12, pp 855–858

From counterfactuals to sufficient component causes and vice versa



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  1. 1.
    Flanders WD. On the relationship of sufficient component cause models with potential outcome (counterfactual) models. EJEP 2006; (in press)Google Scholar
  2. 2.
    Hume D. An Enquiry Concerning Human Understanding (1748). Reprinted LaSalle, IL: Open Court Press, 1958Google Scholar
  3. 3.
    Lewis D (1973) Causation. J. Phil. 70: 556–567CrossRefGoogle Scholar
  4. 4.
    Lewis D (1973) Counterfactuals. Harvard University Press, CambridgeGoogle Scholar
  5. 5.
    Neyman J (1990) Sur les applications de la thar des probabilities aux experiences Agaricales: Essay des principle (1923). Excerpts reprinted in English (Dabrowska D, Speed T, Trans.) in Statistical Science 5: 463–472Google Scholar
  6. 6.
    Rubin DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66: 688–701CrossRefGoogle Scholar
  7. 7.
    Rubin DB (1978) Bayesian inference for causal effects: The role of randomization. Ann. Stat. 6: 34–58Google Scholar
  8. 8.
    Robins JM (1986) A new approach to causal inference in mortality studies with sustained exposure period - application to control of the healthy worker survivor effect. Math. Model. 7: 1393–1512CrossRefGoogle Scholar
  9. 9.
    Robins JM (1987) Addendum to a new approach to causal inference in mortality studies with sustained exposure period—application to control of the healthy worker survivor effect. Comput. Math. Appl. 14: 923–945CrossRefGoogle Scholar
  10. 10.
    Pearl J (1995) Causal diagrams for epidemiologic research. Biometrika 82: 669–688CrossRefGoogle Scholar
  11. 11.
    Greenland S, Pearl J, Robins JM (1999) Causal diagrams for epidemiologic research. Epidemiology 10: 37–48PubMedCrossRefGoogle Scholar
  12. 12.
    Greenland S, Brumback B (2002) An overview of relations among causal modelling methods. Int. J. Epidemiol. 31: 1030–1037PubMedCrossRefGoogle Scholar
  13. 13.
    Robins JM, Greenland S (2000) Comment on “Causal inference without counterfactuals” by Dawid AP. J. Am. Stat. Assoc. – Theory and Methods 95: 477–482CrossRefGoogle Scholar
  14. 14.
    Mackie JL (1965) Causes and conditions. Am. Philos. Q. 2: 245–255Google Scholar
  15. 15.
    Rothman KJ (1976) Causes. Am. J. Epidemiol. 104: 587–592PubMedGoogle Scholar
  16. 16.
    Greenland S, Poole C (1988) Invariants and noninvariants in the concept of interdependent effects. Scand J. Work Environ. Health 14:125–129PubMedGoogle Scholar
  17. 17.
    Aickin M. Causal Analysis in Biomedicine and Epidemiology Based on Minimal Sufficient Causation. New York: Marcel Dekker, 2002Google Scholar
  18. 18.
    Novick LR, Cheng PW (2004) Assessing interactive causal influence. Psychol. Rev. 111: 455–485PubMedCrossRefGoogle Scholar
  19. 19.
    Hernán MA (2004) A definition of causal effect for epidemiological research. J. Epidemiol. Community Health 58: 265–271PubMedCrossRefGoogle Scholar
  20. 20.
    Rothman K, Greenland S (1998) Modern Epidemiology, 2nd edn. Lippincott-Raven, PhiladelphiaGoogle Scholar
  21. 21.
    Koopman J (1981) Interaction between discrete causes. Am. J. Epidemiol. 113: 716–724PubMedGoogle Scholar
  22. 22.
    VanderWeele TJ, Robins JM. Biologic interactions and their identification. (September 2006). COBRA Preprint Series. Scholar
  23. 23.
    VanderWeele TJ, Robins JM. A theory of sufficient cause interactions. (September 2006). COBRA Preprint Series. Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of Health StudiesUniversity of ChicagoChicagoUSA
  2. 2.Department of EpidemiologyHarvard School of Public HealthBostonUSA

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