Author's reply: The role of potential outcomes thinking in assessing mediation and interaction.

ator matters when estimating indirect effects. J Epidemiol Community Health 2013;67:458–66. 18. le Cessie S, Debeij J, Rosendaal FR, Cannegieter SC, Vandenbroucke JP. Quantification of bias in direct effects estimates due to different types of measurement error in the mediator. Epidemiology 2012;23:551–60. 19. VanderWeele TJ, Asomaning K, Tchetgen EJT et al. Genetic variants on 15q25.1, smoking, and lung cancer: an assessment of mediation and interaction. Am J Epidemiol 2012;175:1013–20. 20. Munafo MR, Timofeeva MN, Morris RW et al. Association between genetic variants on chromosome 15q25 locus and objective measures of tobacco exposure. J Natl Cancer Inst 2012;104(10):740–48. 21. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat Med 2008;27(8):1133–63. 22. Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 2003;32:1–22. 23. Davey Smith G, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol 2004;33: 30–42. 24. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 2014;23:R89–98. 25. Varbo A, Benn M, Davey Smith G, Timpson NJ, TybjaergHansen A, Nordestgaard BG. Remnant cholesterol, lowdensity lipoprotein cholesterol, and blood pressure as mediators from obesity to ischemic heart disease. Circ Res 2015;116: 665–73. 26. VanderWeele TJ, Hernan M. Causal effects and natural laws: towards a conceptualization of causal counterfactuals for nonmanipulable exposures, with applications to the effects of race and sex. In: Berzuini C, Dawid P, Bernardinell C (eds). Causality: Statistical Perspectives and Applications. Hoboken, NJ: John Wiley, 2012. 27. VanderWeele TJ, Robinson WR. On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology 2014;25:473–84. 28. Glymour C, Glymour MR. Race and sex are causes. Epidemiology 2014;25:488–90. 29. Pearce N, Blair A, Vineis P et al. IARC monographs: 40 years of evaluating carcinogenic hazards to humans. Environ Health Perspect 2015;123:507–14. 30. Vandenbroucke JP, Rosing J, Bloemenkamp KWM et al. Oral contraceptives and the risk of venous thrombosis. N Engl J Med 2001;344:1527–35. 31. Vandenbroucke JP, Rosendall FR, Bertina RM. Factor V Leiden, oral contraceptives, and deep vein thrombosis. In: Khoury MJ, Little J, Burke W (eds). Human Genome Epidemiology. Oxford,UK: Oxford University Press, 2003. 32. Haack S. Manifesto of a Passionate Moderate. Chicago, IL: Chicago University Press, 1998. 33. Commenges D, Gegout-Petit A. A general dynamical statistical model with causal interpretation. J R Stat Soc B 2009;71:719–36. 34. Pearce N, Merletti F. Complexity, simplicity and epidemiology. Int J Epidemiol 2006;35:515–19. 35. Gunasekara FI, Richardson K, Carter K, Blakely T. Fixed effects analysis of repeated measures data. Int J Epidemiol 2014;43:264–69. 36. Krieger N. Epidemiology and the People’s Health: Theory and Context. New York, NY: Oxford University Press, 2011. 37. Susser M, Stein Z. Eras in Epidemiology: The Evolution of Ideas. New York, NY: Oxford University Press, 2009. 38. Naimi AI. Invited commentary: boundless science putting natural direct and indirect effects in a clearer empirical context. Am J Epidemiol 2015;182:109–14. 39. Skrondal A. Much ado about interactions. Epidemiology 2014;25:723–26.