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date: 23 August 2019

Abstract and Keywords

There has been in the last decade or so an upsurge of interest in causation outside of philosophy. One important strand of research focuses on how statistical data can be used to draw inferences about causal structures. Central to this approach are ‘causal models’, intended to represent systems of ‘variables’ connected by ‘mechanisms’. By careful appeal to and analysis of such causal models, it is possible to develop subtle ways of empirically testing causal hypotheses in light of statistical data. But two serious problems as yet prevent this approach from attaining the kind of scientific rigour it ought to have. Both are foundational. First, crucial notions — most notably, the notion of a ‘mechanism’ — are left almost wholly obscure, in a way which makes it impossible to say anything general or informative about what makes any given situation apt for description by one causal model rather than another. Secondly, the way causal models are typically used draws no distinction whatsoever between ordinary causal processes and causal connections involving omissions.

Keywords: causation, causal structures, causal models, empirical testing, causal hypothesis, causal processes

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