Библиография

Библиография

Введение: ум важнее данных

Введение: ум важнее данных

Annotated Bibliography

The history of probability and statistics from antiquity to modern days is covered in depth by Hacking (1990); Stigler (1986, 1999, 2016). A less technical account is given in Salsburg (2002). Comprehensive accounts of the history of causal thought are unfortunately lacking, though interesting material can be found in Hoover (2008); Kleinberg (2015); Losee (2012); Mumford and Anjum (2014). The prohibition on causal talk can be seen in almost every standard statistical text, for example, Freedman, Pisani, and Purves (2007) or Efron and Hastie (2016). For an analysis of this prohibition as a linguistic impediment, see Pearl (2009, Chapters 5 and 11), and as a cultural barrier, see Pearl (2000b). Recent accounts of the achievements and limitations of Big Data and machine learning are Darwiche (2017); Pearl (2017); Mayer-Schönberger and Cukier (2013); Domingos (2015); Marcus (July 30, 2017). Toulmin (1961) provides historical context to this debate. Readers interested in “model discovery” and more technical treatments of the do-operator can consult Pearl (1994, 2000a, Chapters 2–3); Spirtes, Glymour, and Scheines (2000). For a gentler introduction, see Pearl, Glymour, and Jewell (2016). This last source is recommended for readers with college-level mathematical skills but no background in statistics or computer science. It also provides basic introduction to conditional probabilities, Bayes’s rule, regression, and graphs.

Earlier versions of the inference engine shown in Figure 1.1 can be found in Pearl (2012); Pearl and Bareinboim (2014).

References

Darwiche, A. (2017). Human-level intelligence or animal-like abilities? Tech. rep., Department of Computer Science, University of California, Los Angeles, CA. Submitted to Communications of the ACM. Accessed online at https://arXiv:1707.04327.

Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books, New York, NY.

Efron, B., and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, New York, NY.

Freedman, D., Pisani, R., and Purves, R. (2007). Statistics. 4th ed. W. W. Norton & Company, New York, NY.

Hacking, I. (1990). The Taming of Chance (Ideas in Context). Cambridge University Press, Cambridge, UK.

Hoover, K. (2008). Causality in economics and econometrics. In The New Palgrave Dictionary of Economics (S. Durlauf and L. Blume, eds.), 2nd ed. Palgrave Macmillan, New York, NY.