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Empirical and counterfactual conditions for sufficient cause interactions

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  • Tyler J. Vanderweele
  • James M. Robins

Abstract

Sufficient-component causes are discussed within the deterministic potential outcomes framework so as to formalize notions of sufficient causes, synergism and sufficient cause interactions. Doing so allows for the derivation of counterfactual and empirical conditions for detecting the presence of sufficient cause interactions. The conditions are novel in that, unlike other conditions in the literature, they make no assumption about monotonicity. The conditions can also be generalized and the conditions for three-way sufficient cause interactions are given explicitly. The statistical tests derived for sufficient cause interactions are compared with and contrasted to interaction terms in standard statistical models. Copyright 2008, Oxford University Press.

Suggested Citation

  • Tyler J. Vanderweele & James M. Robins, 2008. "Empirical and counterfactual conditions for sufficient cause interactions," Biometrika, Biometrika Trust, vol. 95(1), pages 49-61.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:1:p:49-61
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    File URL: http://hdl.handle.net/10.1093/biomet/asm090
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    Cited by:

    1. VanderWeele Tyler J, 2010. "Epistatic Interactions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-24, January.
    2. Jaffer M. Zaidi & Tyler J. VanderWeele, 2021. "On the identification of individual level pleiotropic, pure direct, and principal stratum direct effects without cross world assumptions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 881-907, September.
    3. Jui-Hsiang Lin & Wen-Chung Lee, 2015. "Testing for Mechanistic Interactions in Long-Term Follow-Up Studies," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-10, March.
    4. VanderWeele Tyler J, 2010. "Attributable Fractions for Sufficient Cause Interactions," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-28, February.
    5. Wen-Chung Lee, 2014. "Estimation of a Common Effect Parameter from Follow-Up Data When There Is No Mechanistic Interaction," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-5, January.

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