From Metaphysics to Method: Comments on Manipulability and the Causal Markov Condition

Author: Cartwright, Nancy

Source: British Journal for the Philosophy of Science, Volume 57, Number 1, March 2006 , pp. 197-218(22)

Publisher: Oxford University Press

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Abstract:

Daniel Hausman and James Woodward claim to prove that the causal Markov condition, so important to Bayes-nets methods for causal inference, is the `flip side' of an important metaphysical fact about causation—that causes can be used to manipulate their effects. This paper disagrees. First, the premise of their proof does not demand that causes can be used to manipulate their effects but rather that if a relation passes a certain specific kind of test, it is causal. Second, the proof is invalid. Third, the kind of testability they require can easily be had without the causal Markov condition. <LIST> <ITEM>

Introduction </ITEM> <ITEM>

Earlier views: manipulability v testability </ITEM> <ITEM>

Increasingly weaker theses </ITEM> <ITEM>

The proof is invalid </ITEM> <ITEM>

MOD* is implausible </ITEM> <ITEM>

Two alternative claims and their defects </ITEM> <ITEM>

A true claim and a valid argument </ITEM> <ITEM>

Indeterminism </ITEM> <ITEM>

Overall conclusion </ITEM></LIST>

Keywords: machine learning; network routing; adaptive routing

Document Type: Research article

DOI: 10.1093/bjps/axi156

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