By Nancy Cartwright
Searching motives and utilizing Them argues that causation isn't really something, as typically assumed, yet many. there's a large number of causal kinfolk, every one with diverse characterizing good points, diversified tools for discovery and assorted makes use of to which it may be placed. during this selection of new and formerly released essays, Nancy Cartwright offers a severe survey of philosophical and fiscal literature on causality, with a distinct specialise in the presently trendy Bayes-nets and invariance tools - and it exposes a massive hole in that literature. virtually each account treats both solely the right way to hunt explanations or easy methods to use them. yet the place is the bridge among? it really is no reliable understanding how one can warrant a causal declare if we do not be aware of what we will do with that declare when we have it. This booklet will curiosity philosophers, economists and social scientists.
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Extra info for Hunting Causes and Using Them: Approaches in Philosophy and Economics
Then we devise specific models to consider specific causal hypotheses. Does loss of skill among workers during periods of unemployment perpetuate periods of unemployment? One model5 to test this hypothesis supposes that workers gain utility only from wages and leisure and entrepreneurs only from profit, that job–worker matching occurs in a specific way, that there are just two generations in the labour market, that everyone is hired/rehired at once, etc. In this model, the hypothesis can be proved true.
See ch. 10 in this book for details. 15 Here we must be careful to avoid a logical mistake. If the premises of a deductive argument are true, the conclusion must be true. What if we do not know they are true but are only willing to assign a probability to them? If we assign a probability of say 90 per cent to the premises taken jointly and we do not know anything else relevant, then it is reasonable to assign a probability of 90 per cent to the conclusion. That however is very different from the case where we are fairly certain, may even take ourselves to know, nine out of ten of the premises, but have strong reason to deny the tenth.
1 We need not look just to ‘high’ theory, abstractly expressed and systematically organized. For instance, as Naomi Oreskes argues,2 it would be a mistake to think that we do not know the harmful effects of greenhouse gases just because 2 Oreskes (forthcoming). 28 Plurality in causality the results may not be derivable from this or that cutting-edge model. 3 This is not ‘high’ theory – this is no cutting-edge climate model – but it is good science, science that has been known and accepted for a long time, based on physics theory, confirmed by laboratory experiments, etc.