The complex and fascinating story of turbo and low-density parity-check codes has not been told in a truly layman-friendly form, but good starting points are Costello and Forney (2007) and Hardesty (2010a, 2010b). The crucial realization that turbo codes work by the belief propagation algorithm stems from McEliece, David, and Cheng (1998). Efficient codes continue to be a battleground for wireless communications; Carlton (2016) takes a look at the current contenders for “5G” phones (due out in the 2020s).
References
Breast Cancer Surveillance Consortium (BCSC). (2009). Performance measures for 1,838,372 screening mammography examinations from 2004 to 2008 by age. Available at: http://www.bcsc-research.org/statistics/performance/screening/2009/perf_age.html (accessed October 12, 2016).
Carlton, A. (2016). Surprise! Polar codes are coming in from the cold. Computerworld. Available at: https://www.computerworld.com/article/3151866/mobile-wireless/surprise-polar-codes-are-coming-in-from-the-cold.html (posted December 22, 2016).
Clark, N., and Kramer, A. (October 14, 2015). Malaysia Airlines Flight 17 most likely hit by Russian-made missile, inquiry says. New York Times.
Conrady, S., and Jouffe, L. (2015). Bayesian Networks and Bayesia Lab: A Practical Introduction for Researchers. Bayesia USA, Franklin, TN.
Costello, D. J., and Forney, G. D., Jr. (2007). Channel coding: The road to channel capacity. Proceedings of IEEE 95: 1150–1177. Hardesty, L. (2010a). Explained: Gallager codes. MIT News. Available at: http://news.mit.edu/2010/gallager-codes-0121 (posted: January 21, 2010).
Hardesty, L. (2010b). Explained: The Shannon limit. MIT News. Available at: http://news.mit.edu/2010/explained-shannon-0115 (posted January 19, 2010).
Lauritzen, S., and Spiegelhalter, D. (1988). Local computations with probabilities on graphical structures and their application to expert systems (with discussion). Journal of the Royal Statistical Society, Series B 50: 157–224.
Lindley, D. V. (2014). Understanding Uncertainty. Rev. ed. John Wiley and Sons, Hoboken, NJ.
McEliece, R. J., David, J. M., and Cheng, J. (1998). Turbo decoding as an instance of Pearl’s “belief propagation” algorithm. IEEE Journal on Selected Areas in Communications 16: 140–152.
Pearl, J. (1985). Bayesian networks: A model of self-activated memory for evidential reasoning. In Proceedings, Cognitive Science Society (CSS-7). UCLA Computer Science Department, Irvine, CA.
Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence 29: 241–288.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA.
Pearl, J., Glymour, M., and Jewell, N. (2016). Causal Inference in Statistics: A Primer. Wiley, New York, NY.
Pearl, J., and Paz, A. (1985). GRAPHOIDS: A graph-based logic for reasoning about relevance relations. Tech. Rep. 850038 (R-53-L). Computer Science Department, University of California, Los Angeles. Short version in B. DuBoulay, D. Hogg, and L. Steels (Eds.) Advances in Artificial Intelligence — II, Amsterdam, North Holland, 357–363, 1987.
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Wiegerinck, W., Burgers, W., and Kappen, B. (2013). Bayesian networks, introduction and practical applications. In Handbook on Neural Information Processing (M. Bianchini, M. Maggini, and L. C. Jain, eds.). Intelligent Systems Reference Library (Book 49). Springer, Berlin, Germany, 401–431.
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.
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.