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European Journal of Epidemiology

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01.05.2009 | Commentary

Mediation and mechanism

verfasst von: Tyler J. VanderWeele

Erschienen in: European Journal of Epidemiology | Ausgabe 5/2009

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Abstract

The concepts of mediation and mechanism are contrasted and logical implications holding between theses two concepts are described. The concept of mediation can be formalized using counterfactual definitions of indirect effects; the concept of mechanism can be formalized within the sufficient cause framework. It is shown that both concepts can be illustrated using a single causal diagram. It is also shown that mediation implies mechanism but mechanism need not imply mediation. Discussion is given regarding how the distinction between “statistical causality” and “mechanistic causality” is blurred by recent work in causal inference concerning methods for testing for mediation and mechanism.

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Hafeman D. A sufficient cause based approach to the assessment of mediation. Eur J Epidemiol. 2008;23:711–21. doi:10.1007/s10654-008-9286-7.PubMedCrossRef

Judd CM, Kenny DA. Estimating mediation in treatment evaluations. Process Anal. 1981;5:602–19.

Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82. doi:10.1037/0022-3514.51.6.1173.PubMedCrossRef

Bollen KA. Total, direct and indirect effects in structural equation models. In: Clogg CC, editor. Sociological methodology. Washington, DC: American Sociological Association; 1987. p. 37–69.

Holland PW. Causal inference, path analysis, and recursive structural equations models. In: Clogg CC, editor. Sociological methodology. Washington, DC: American Sociological Association; 1988. p. 449–84.

Sobel ME. Effect analysis and causation in linear structural equation models. Psychometrika. 1990;55:495–515. doi:10.1007/BF02294763.CrossRef

Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology. 1992;3:143–55. doi:10.1097/00001648-199203000-00013.PubMedCrossRef

Pearl J. Direct and indirect effects. In: Proceedings of the seventeenth conference on uncertainty and artificial intelligence. San Francisco: Morgan Kaufmann; 2001:411-420.

Kaufman JS, MacLehose RF, Kaufman S. A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. Epidemiol Perspect Innov. 2004;1:4. doi:10.1186/1742-5573-1-4.PubMedCrossRef

10.

Peterson ML, Sinisi SE, van der Laan MJ. Estimation of direct causal effects. Epidemiology. 2006;17:276–84. doi:10.1097/01.ede.0000208475.99429.2d.CrossRef

11.

VanderWeele TJ. Marginal structural models for the estimation of direct and indirect effects. Epidemiology. 2009;20:18–26. doi:10.1097/EDE.0b013e31818f69ce.PubMedCrossRef

12.

Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58:21–9. doi:10.1111/j.0006-341X.2002.00021.x.PubMedCrossRef

13.

Rubin DB. Direct and indirect effects via potential outcomes. Scand J Stat. 2004;31:161–70. doi:10.1111/j.1467-9469.2004.02-123.x.CrossRef

14.

VanderWeele TJ. Simple relations between principal stratification and direct and indirect effects. Stat Probab Lett. 2008;78:2957–62. doi:10.1016/j.spl.2008.05.029.CrossRef

15.

Rothman KJ. Causes. Am J Epidemiol. 1976;104:587–92.PubMed

16.

VanderWeele TJ, Robins JM. The identification of synergism in the sufficient-component cause framework. Epidemiol. 2007;18:329–39. doi:10.1097/01.ede.0000260218.66432.88.CrossRef

17.

VanderWeele TJ, Robins JM. Empirical and counterfactual conditions for sufficient cause interactions. Biometrika. 2008;95:49–61. doi:10.1093/biomet/asm090.CrossRef

18.

Cayley A. On a question in the theory of probabilities. Philos Mag. 1853;6:259.

19.

Mackie JL. Causes and conditions. Am Philos Q. 1965;2:245–55.

20.

MacMahon B, Pugh TF. Causes and entities of disease. In: Clark DW, MacMahon B, editors. Preventive medicine. Boston: Little, Brown, and Company; 1967. p. 11–8.

21.

VanderWeele TJ, Robins JM. Directed acyclic graphs, sufficient causes and the properties of conditioning on a common effect. Am J Epidemiol. 2007;166:1096–104. doi:10.1093/aje/kwm179.PubMedCrossRef

22.

VanderWeele TJ, Robins JM. Minimal sufficient causation and directed acyclic graphs. Ann Stat. 2009. (in press).

23.

Hall N, Paul LA. Causation and preemption. In: Clark P, Hawley K, editors. Philosophy of science today. Oxford: Oxford University Press; 2003. p. 100–29.

24.

Greenland S, Robins JM. Conceptual problems in the definition and interpretation of attributable fractions. Am J Epidemiol. 1988;128:1185–97.PubMed

25.

Greenland S, Brumback B. An overview of relations among causal modeling methods. Int J Epidemiol. 2002;31:1030–7. doi:10.1093/ije/31.5.1030.PubMedCrossRef

26.

Hoffmann K, Heidemann C, Weikert C, Schulze MB, Boeing H. Estimating the proportion of disease due to classes of sufficient causes. Am J Epidemiol. 2006;163:76–83. doi:10.1093/aje/kwj011.PubMedCrossRef

27.

Aalen OO, Frigessi A. What can statistics contribute to a causal understanding? Scand J Stat. 2007;34:155–68. doi:10.1111/j.1467-9469.2006.00549.x.CrossRef

28.

Heckman JJ. The scientific model of causality. Sociol Methodol. 2005;1:1–98. doi:10.1111/j.0081-1750.2006.00163.x.CrossRef

29.

Heckman JJ. Econometric causality. Int Stat Rev. 2007;76:1–27. doi:10.1111/j.1751-5823.2007.00024.x.CrossRef

30.

Machamer P, Darden L, Craver CF. Thinking about mechanisms. Philos Sci. 2000;67:1–25. doi:10.1086/392759.CrossRef

31.

Pearl J. Casual diagrams for empirical research (with discussion). Biometrika. 1995;82:669–710. doi:10.1093/biomet/82.4.669.CrossRef

32.

Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48. doi:10.1097/00001648-199901000-00008.PubMedCrossRef

33.

Glymour MM, Greenland S. Causal diagrams. In: Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. Philadelphia: Lippincott Williams and Wilkins; 2008. p. 183–209.

34.

Robins JM. Testing and estimation of direct effects by reparameterizing directed acyclic graphs with structural nested models. In: Glymour C, Cooper GF, editors. Computation, causation, and discovery. Menlo Park, CA, Cambridge, MA: AAAI Press/The MIT Press; 1999. p. 349–405.

35.

Robins JM. Semantics of causal DAG models and the identification of direct and indirect effects. In: Green P, Hjort NL, Richardson S, editors. Highly structured stochastic systems. New York: Oxford University Press; 2003. p. 70–81.

36.

Joffe M, Small D, Hsu CY. Defining and estimating intervention effects for groups that will develop an auxiliary outcome. Stat Sci. 2007;22:74–97. doi:10.1214/088342306000000655.CrossRef

37.

VanderWeele TJ. Sufficient cause interactions and statistical interactions. Epidemiology. 2009;20:6–13. doi:10.1097/EDE.0b013e31818f69e7.PubMedCrossRef

38.

Cox DR, Wenmuth N. Causality: a statistical view. Int Stat Rev. 2004;72:285–305.

Titel: Mediation and mechanism
verfasst von: Tyler J. VanderWeele
Publikationsdatum: 01.05.2009
Verlag: Springer Netherlands
Erschienen in: European Journal of Epidemiology / Ausgabe 5/2009
Print ISSN: 0393-2990
Elektronische ISSN: 1573-7284
DOI: https://doi.org/10.1007/s10654-009-9331-1

Live-Webinar "Urologie und Sexualmedizin in der Praxis"

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Mediation and mechanism

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Live-Webinar "Urologie und Sexualmedizin in der Praxis"

Springer Medizin

Abstract

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