Думай «почему?». Причина и следствие как ключ к мышлению - читать онлайн книгу. Автор: Джудиа Перл, Дана Маккензи cтр.№ 106

The fate of path analysis in economics and social science is narrated in Chapter 5 of Pearl (2000) and in Bollen and Pearl (2013). Blalock (1964), Duncan (1966), and Goldberger (1972) introduced Wright’s ideas to social science with great enthusiasm, but their theoretical underpinnings were not well articulated. A decade later, when Freedman (1987) challenged path analysts to explain how interventions are modelled, the enthusiasm disappeared, and leading researchers retreated to viewing SEM as an exercise in statistical analysis. This revealing discussion among twelve scholars is documented in the same issue of the Journal of Educational Statistics as Freedman’s article.

The reluctance of economists to embrace diagrams and structural notation is described in Pearl (2015). The painful consequences for economic education are documented in Chen and Pearl (2013).

A popular exposition of the Bayesian-versus-frequentist debate is given in McGrayne (2011).

More technical discussions can be found in Efron (2013) and Lindley (1987).

References

Blalock, H., Jr. (1964). Causal Inferences in Nonexperimental Research. University of North Carolina Press, Chapel Hill, NC.

Bollen, K., and Pearl, J. (2013). Eight myths about causality and structural equation models. In Handbook of Causal Analysis for Social Research (S. Morgan, ed.). Springer, Dordrecht, Netherlands, 301–328.

Chen, B., and Pearl, J. (2013). Regression and causation: A critical examination of econometrics textbooks. Real-World Economics Review 65: 2–20.

Crow, J. F. (1982). Sewall Wright, the scientist and the man. Perspectives in Biology and Medicine 25: 279–294.

Crow, J. F. (1990). Sewall Wright’s place in twentieth-century biology. Journal of the History of Biology 23: 57–89.

Duncan, O. D. (1966). Path analysis. American Journal of Sociology 72: 1–16.

Efron, B. (2013). Bayes’ theorem in the 21st century. Science 340: 1177–1178.

Freedman, D. (1987). As others see us: A case study in path analysis (with discussion). Journal of Educational Statistics 12: 101–223. Galton, F. (1869). Hereditary Genius. Macmillan, London, UK. Galton, F. (1883). Inquiries into Human Faculty and Its Development.

Macmillan, London, UK.

Galton, F. (1889). Natural Inheritance. Macmillan, London, UK.

Goldberger, A. (1972). Structural equation models in the social sciences. Econometrica: Journal of the Econometric Society 40: 979–1001.

Lindley, D. (1987). Bayesian Statistics: A Review. CBMS-NSF Regional Conference Series in Applied Mathematics (Book 2). Society for Industrial and Applied Mathematics, Philadelphia, PA.

McGrayne, S. B. (2011). The Theory That Would Not Die. Yale University Press, New Haven, CT.

Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, NY.

Pearl, J. (2015). Trygve Haavelmo and the emergence of causal calculus. Econometric Theory 31: 152–179. Special issue on Haavelmo centennial.

Provine, W. B. (1986). Sewall Wright and Evolutionary Biology. University of Chicago Press, Chicago, IL.

Stigler, S. M. (2012). Studies in the history of probability and statistics, L: Karl Pearson and the rule of three. Biometrika 99: 1–14.

Stigler, S. M. (2016). The Seven Pillars of Statistical Wisdom. Harvard University Press, Cambridge, MA.

Wikipedia. (2016a). Hardy-Weinberg principle. Available at: https://en.wikipedia.org/wiki/Hardy-Weinberg-principle (last edited: Oc- tober 2, 2016).

Wikipedia. (2016b). Galileo Galilei. Available at: https://en.wikipedia.org/wiki/Galileo_Galilei (last edited: October 6, 2017).

Wright, S. (1920). The relative importance of heredity and environment in determining the piebald pattern of guinea-pigs. Proceedings of the National Academy of Sciences of the United States of America 6: 320–332.

Wright, S. (1921). Correlation and causation. Journal of Agricultural Research 20: 557–585.

Wright, S. (1983). On “Path analysis in genetic epidemiology: A critique.” American Journal of Human Genetics 35: 757–768.

Annotated Bibliography

Elementary introductions to Bayes’s rule and Bayesian thinking can be found in Lindley (2014) and Pearl, Glymour, and Jewell (2016).

Debates with competing representations of uncertainty are presented in Pearl (1988); see also the extensive list of references given there.

Our mammogram data are based primarily on information from the Breast Cancer Surveillance Consortium (BCSC, 2009) and US Preventive Services Task Force (USPSTF, 2016) and are presented for instructional purposes only.

“Bayesian networks” received their name in 1985 (Pearl, 1985) and were first presented as a model of self-activated memory. Applications to expert systems followed the development of belief updating algorithms for loopy networks (Pearl, 1986; Lauritzen and Spiegelhalter, 1988).

The concept of d-separation, which connects path blocking in a diagram to dependencies in the data, has its roots in the theory of graphoids (Pearl and Paz, 1985). The theory unveils the common properties of graphs (hence the name) and probabilities and explains why these two seemingly alien mathematical objects can support one another in so many ways. See also “Graphoid,” Wikipedia.

The amusing example of the bag on the airline flight can be found in Conrady and Jouffe (2015, Chapter 4).

The Malaysia Airlines Flight 17 disaster was well covered in the media; see Clark and Kramer (October 14, 2015) for an update on the investigation a year after the incident. Wiegerinck, Burgers, and Kappen (2013) describes how Bonaparte works. Further details on the identification of Flight 17 victims, including the pedigree shown in Figure 3.7, came from personal correspondence from W. Burgers to D. Mackenzie (August 24, 2016) and from a phone interview with W. Burgers and B. Kappen by D. Mackenzie (August 23, 2016).

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).