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Pearl, J., Glymour, M., and Jewell, N. (2016). Causal Inference in Statistics: A Primer. Wiley, New York, NY.

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Глава 4. Осложнители и наоборот: как убить прячущуюся переменную

Annotated Bibliography

The story of Daniel has frequently been cited as the first controlled trial; see, for example, Lilienfeld (1982) or Stigler (2016). The results of the Honolulu walking study were reported in Hakim (1998).

Fisher Box’s lengthy quote about “the skillful interrogation of Nature” comes from her excellent biography of her father (Box, 1978, Chapter 6). Fisher, too, wrote about experiments as a dialogue with Nature; see Stigler (2016). Thus I believe we can think of her quote as nearly coming from the patriarch himself, only more beautifully expressed.

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.

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