From counterfactuals to sufficient component causes and vice versa

European Journal of Epidemiology

Volumn 21, Issue 12, 2006, Pages 855-858

  VanderWeele, Tyler J ^a   Hernán, Miguel A ^b  

^a UNIVERSITY OF CHICAGO   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ANALYSIS; MATHEMATICAL MODEL; MEDICAL LITERATURE; MEDICAL RESEARCH; NOTE; OUTCOME ASSESSMENT; RISK ASSESSMENT; STATISTICAL ANALYSIS; STATISTICAL MODEL;

CAUSALITY; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC RESEARCH DESIGN; HUMANS; MODELS, STATISTICAL;

EID: 33846664089     PISSN: 03932990     EISSN: 15737284     Source Type: Journal    
DOI: 10.1007/s10654-006-9075-0     Document Type: Note

References (23)

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