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.
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Balke, A., and Pearl, J. (1994b). Probabilistic evaluation of counterfactual queries. In Proceedings of the Twelfth National Conference on Artificial Intelligence, vol. 1. MIT Press, Menlo Park, CA, 230–237.
Cowles, M. (2016). Statistics in Psychology: An Historical Perspective. 2nd ed. Routledge, New York, NY.
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Freedman, D. (1987). As others see us: A case study in path analysis (with discussion). Journal of Educational Statistics 12: 101–223. Greenland, S. (1999). Relation of probability of causation, relative risk, and doubling dose: A methodologic error that has become a social problem. American Journal of Public Health 89: 1166–1169. Greiner, D. J. (2008). Causal inference in civil rights litigation. Harvard Law Review 81: 533–598.
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Halpern, J. (2016). Actual Causality. MIT Press, Cambridge, MA. Hannart, A., Pearl, J., Otto, F., Naveu, P., and Ghil, M. (2016).
Causal counterfactual theory for the attribution of weather and climate-related events. Bulletin of the American Meteorological Society (BAMS) 97: 99–110.