Думай «почему?». Причина и следствие как ключ к мышлению

Morgan, S., and Winship, C. (2007). Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research). Cambridge University Press, New York, NY.

Pearl, J. (2009). Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press, New York, NY.

Pearl, J. (2013). Reflections on Heckman and Pinto’s “Causal analysis after Haavelmo.” Tech. Rep. R-420. Department of Computer Science, University of California, Los Angeles, CA. Working paper.

Pearl, J. (2015). Indirect confounding and causal calculus (on three papers by Cox and Wermuth). Tech. Rep. R-457. Department of Computer Science, University of California, Los Angeles, CA.

Shpitser, I., and Pearl, J. (2006a). Identification of conditional interventional distributions. In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (R. Dechter and T. Richardson, eds.). AUAI Press, Corvallis, OR, 437–444.

Shpitser, I., and Pearl, J. (2006b). Identification of joint interventional distributions in recursive semi-Markovian causal models. In Proceedings of the Twenty-First National Conference on Artificial Intelligence. AAAI Press, Menlo Park, CA, 1219–1226.

Stock, J., and Trebbi, F. (2003). Who invented instrumental variable regression? Journal of Economic Perspectives 17: 177–194.

Textor, J., Hardt, J., and Knüppel, S. (2011). DAGitty: A graphical tool for analyzing causal diagrams. Epidemiology 22: 745.

Tian, J., and Pearl, J. (2002). A general identification condition for causal effects. In Proceedings of the Eighteenth National Conference on Artificial Intelligence. AAAI Press/MIT Press, Menlo Park, CA, 567–573.

Wermuth, N., and Cox, D. (2008). Distortion of effects caused by indirect confounding. Biometrika 95: 17–33. (See Pearl [2009, Chapter 4] for a general solution.)

Wermuth, N., and Cox, D. (2014). Graphical Markov models: Overview. ArXiv: 1407.7783.

White, H., and Chalak, K. (2009). Settable systems: An extension of Pearl’s causal model with optimization, equilibrium and learning. Journal of Machine Learning Research 10: 1759–1799.

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Глава 8. Контрфактивные суждения: глубинный анализ миров, которые могли бы существовать

Annotated Bibliography

The definition of counterfactuals as derivatives of structural equations was introduced by Balke and Pearl (1994a, 1994b) and was used to estimate probabilities of causation in legal settings. The relationships between this framework and those developed by Rubin and Lewis are discussed at length in Pearl (2000, Chapter 7), where they are shown to be logically equivalent; a problem solved in one framework would yield the same solution in another.

Recent books in social science (e.g., Morgan and Winship, 2015) and in health science (e.g., VanderWeele, 2015) are taking the hybrid, graph-counterfactual approach pursued in our book.

The section on linear counterfactuals is based on Pearl (2009, pp. 389–391), which also provides the solution to the problem posed in note 12. Our discussion of ETT is based on Shpitser and Pearl (2009). Legal questions of attribution, as well as probabilities of causation, are discussed at length in Greenland (1999), who pioneered the counterfactual approach to such questions. Our treatment of PN, PS, and PNS is based on Tian and Pearl (2000) and Pearl (2009, Chapter 9). A gentle approach to counterfactual attribution, including a tool kit for estimation, is given in Pearl, Glymour, and Jewell (2016). An advanced formal treatment of actual causation can be found in Halpern (2016).

Matching techniques for estimating causal effects are used routinely by potential outcome researchers (Sekhon, 2007), though they usually ignore the pitfalls shown in our education-experience-salary example. My realization that missing-data problems should be viewed in the context of causal modeling was formed through the analysis of Mohan and Pearl (2014).

Cowles (2016) and Reid (1998) tell the story of Neyman’s tumultuous years in London, including the anecdote about Fisher and the wooden models. Greiner (2008) is a long and substantive introduction to “but-for” causation in the law. Allen (2003), Stott et al. (2013), Trenberth (2012), and Hannart et al. (2016) address the problem of attribution of weather events to climate change, and Hannart in particular invokes the ideas of necessary and sufficient probability, which bring more clarity to the subject.

References

Allen, M. (2003). Liability for climate change. Nature 421: 891–892. Balke, A., and Pearl, J. (1994a). Counterfactual probabilities: Computational methods, bounds, and applications. In Uncertainty in Artificial Intelligence 10 (R. L. de Mantaras and D. Poole, eds.). Morgan Kaufmann, San Mateo, CA, 46–54.