Думай «почему?». Причина и следствие как ключ к мышлению - Джудиа Перл
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It is fascinating to read Weinberg’s papers on confounding (Weinberg, 1993; Howards et al., 2012) back-to-back. They are like two snapshots of the history of confounding, one taken just before causal diagrams became widespread and the second taken twenty years later, revisiting the same examples using causal diagrams. Forbes’s complicated diagram of the causal network for asthma and smoking can be found in Williamson et al. (2014).
Morabia’s “classic epidemiological definition of confounding” can be found in Morabia (2011). The quotes from David Cox come from Cox (1992, pp. 66–67). Other good sources on the history of confounding are Greenland and Robins (2009) and Wikipedia (2016).
The back-door criterion for eliminating confounding bias, together with its adjustment formula, were introduced in Pearl (1993). Its impact on epidemiology can be seen through Greenland, Pearl, and Robins (1999). Extensions to sequential interventions and other nuances are developed in Pearl (2000, 2009) and more gently described in Pearl, Glymour, and Jewell (2016). Software for computing causal effects using do-calculus is available in Tikka and Karvanen (2017).
The paper by Greenland and Robins (1986) was revisited by the authors a quarter century later, in light of the extensive developments since that time, including the advent of causal diagrams (Greenland and Robins, 2009).
References
Box, J. F. (1978). R. A. Fisher: The Life of a Scientist. John Wiley and Sons, New York, NY.
Cox, D. (1992). Planning of Experiments. Wiley-Interscience, New York, NY.
Greenland, S., Pearl, J., and Robins, J. (1999). Causal diagrams for epidemiologic research. Epidemiology 10: 37–48.
Greenland, S., and Robins, J. (1986). Identifiability, exchangeability, and epidemiological confounding. International Journal of Epidemiology 15: 413–419.
Greenland, S., and Robins, J. (2009). Identifiability, exchangeability, and confounding revisited. Epidemiologic Perspectives & Innovations 6. doi:10.1186/1742-5573-6-4.
Hakim, A. (1998). Effects of walking on mortality among nonsmoking retired men. New England Journal of Medicine 338: 94–99.
Hernberg, S. (1996). Significance testing of potential confounders and other properties of study groups — Misuse of statistics. Scandinavian Journal of Work, Environment and Health 22: 315–316.
Howards, P. P., Schisterman, E. F., Poole, C., Kaufman, J. S., and Weinberg, C. R. (2012). “Toward a clearer definition of confounding” revisited with directed acyclic graphs. American Journal of Epidemiology 176: 506–511.
Lilienfeld, A. (1982). Ceteris paribus: The evolution of the clinical trial. Bulletin of the History of Medicine 56: 1–18.
Morabia, A. (2011). History of the modern epidemiological concept of confounding. Journal of Epidemiology and Community Health 65: 297–300.
Pearl, J. (1993). Comment: Graphical models, causality, and intervention. Statistical Science 8: 266–269.
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, NY.
Pearl, J. (2009). Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press, New York, NY.
Pearl, J., Glymour, M., and Jewell, N. (2016). Causal Inference in Statistics: A Primer. Wiley, New York, NY.
Stigler, S. M. (2016). The Seven Pillars of Statistical Wisdom. Harvard University Press, Cambridge, MA.
Tikka, J., and Karvanen, J. (2017). Identifying causal effects with the R Package causaleffect. Journal of Statistical Software 76, no. 12. doi:10.18637/jss.r076.i12.
Weinberg, C. (1993). Toward a clearer definition of confounding.
American Journal of Epidemiology 137: 1–8.
Wikipedia. (2016). Confounding. Available at: https://en.wikipedia.org/wiki/Confounding (accessed: September 16, 2016). Williamson, E., Aitken, Z., Lawrie, J., Dharmage, S., Burgess, H., and Forbes, A. (2014). Introduction to causal diagrams for confounder selection. Respirology 19: 303–311.
Глава 5. Дымные дебаты: на свежий воздух
Annotated Bibliography
Two book-length studies, Brandt (2007) and Proctor (2012a), contain all the information any reader could ask for about the smoking — lung cancer debate, short of reading the actual tobacco company documents (which are available online). Shorter surveys of the smoking-cancer debate in the 1950s are Salsburg (2002, Chapter 18), Parascandola (2004), and Proctor (2012b). Stolley (1991) takes a look at the unique role of R. A. Fisher, and Greenhouse (2009) comments on Jerome Cornfield’s importance. The shot heard around the world was Doll and Hill (1950), which first implicated smoking in lung cancer; though technical, it is a scientific classic.
For the story of the surgeon general’s committee and the emergence of the Hill guidelines for causation, see Blackburn and Labarthe (2012) and Morabia (2013). Hill’s own description of his criteria can be found in Hill (1965).
Lilienfeld (2007) is the source of the “Abe and Yak” story with which we began the chapter.
VanderWeele (2014) and Hernández-Díaz, Schisterman, and Hernán (2006) resolve the birth-weight paradox using causal diagrams. An interesting “before-and-after” pair of articles is Wilcox (2001, 2006), written before and after the author learned about causal diagrams; his excitement in the latter article is palpable.
Readers interested in the latest statistics and historical trends in cancer mortality and smoking may consult US Department of Health and Human Services (USDHHS, 2014), American Cancer Society (2017), and Wingo (2003).
References
American Cancer Society. (2017). Cancer facts and figures. Available at: https://www.cancer.org/research/cancer-facts-statistics.html (posted: February 19, 2015).
Blackburn, H., and Labarthe, D. (2012). Stories from the evolution of guidelines for causal inference in epidemiologic associations: 1953–1965. American Journal of Epidemiology 176: 1071–1077. Brandt, A. (2007). The Cigarette Century. Basic Books, New York, NY.
Doll, R., and Hill, A. B. (1950). Smoking and carcinoma of the lung. British Medical Journal 2: 739–748.
Greenhouse, J. (2009). Commentary: Cornfield, epidemiology, and causality. International Journal of Epidemiology 38: 1199–1201.
Hernández-Díaz, S., Schisterman, E., and Hernán, M. (2006). The birth weight “paradox” uncovered? American Journal of Epidemiology 164: 1115–1120.
Hill, A. B. (1965). The environment and disease: Association or causation? Journal of the Royal Society of Medicine 58: 295–300.
Lilienfeld, A. (2007). Abe and Yak: The interactions of Abraham M.
Lilienfeld and Jacob Yerushalmy in the development of modern epidemiology (1945–1973). Epidemiology 18: 507–514.
Morabia, A. (2013). Hume, Mill, Hill, and the sui generis epidemiologic approach to causal inference. American Journal of Epidemiology
