Singular causation and model reduction

In my previous post I tried to get clear about when variables could be safely removed from a causal model without affecting what the model is capable of telling us about singular causal relations. There, I endorsed two principles stating when causal models may be reduced by excising variables in a particular way. If we endorse these principles, and we want to give a theory of singular causation formulated in terms of correct causal models, then we should want that theory to give the very same verdicts before and after model reduction. The point of today’s post is that there is a wide family of theories of causation which run afoul of this constraint. Those theories will say that two variable values are causally related in one model, but reverse this judgment when the model is reduced.

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When can variables be safely removed from a causal model?

Much of our causal talk consists of sentences of the form “c caused e”, where both c and e are token, non-repeatable events or facts or what-have-you (there will be disagreement about what kinds of things ‘c’ and ‘e’ denote, but for now, I’ll just call them ‘events’). Let’s call the kinds of causal relations we’re talking about with sentences like those ‘singular causal relations’. The topic of causation is not exhausted by singular causal relations.

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