Abstract
Association is not causation. Many have held that Statistics, while well suited to investigate the former, strays into treacherous waters when it makes claims to say anything meaningful about the latter. Yet others have proceeded as if inference about the causes of observed phenomena were indeed a valid object of statistical enquiry; and it is certainly a great temptation for statisticians to attempt such ‘causal inference’. Among those who have taken the logic of causal statistical inference seriously I mention in particular Rubin (1974, 1978), Holland (1986), Robins (1986, 1987), Pearl (1995a) and Shafer (1996). This paper represents my own attempt to contribute to the debate as to what are the appropriate statistical models and methods to use for causal inference, and what causal conclusions can be justified by statistical analysis.
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Dawid, A.P. (2001). Causal Inference without Counterfactuals. In: Corfield, D., Williamson, J. (eds) Foundations of Bayesianism. Applied Logic Series, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1586-7_3
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DOI: https://doi.org/10.1007/978-94-017-1586-7_3
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