Causation, Evidence, and Inference by Julian Reiss

By Julian Reiss

In this ebook, Reiss argues in prefer of a decent healthy among facts, idea and function in our causal investigations within the sciences. there is not any doubt that the sciences hire an unlimited array of suggestions to handle causal questions similar to managed experiments, randomized trials, statistical and econometric instruments, causal modeling and inspiration experiments. yet how do those diversified equipment relate to one another and to the causal inquiry to hand? Reiss argues that there's no "gold commonplace" in settling causal matters opposed to which different equipment will be measured. fairly, a number of the equipment of inference are usually reliable purely relative to convinced interpretations of the notice "cause", and every interpretation, in flip, is helping to deal with a few salient function (prediction, clarification or coverage research) yet no longer others. the most aim of this publication is to discover the metaphysical and methodological results of this view within the context of diverse situations stories from the typical and social sciences.

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Unfortunately, it frequently gets its assessments of warrant wrong. Suppose that background knowledge tells us that learning that ‘I and D are correlated’ (e) raises the probability that ‘I causes D’ (h) and thus constitutes evidence according to the Bayesian theory. The problem is that the correlation between I and D is at best a sign of the truth of the hypothesis, not in itself a good reason to infer it. Understood as a theory of warrant, Bayesianism is thus too weak, just as Achinstein argues (cf.

019627 ≈€2% The positive test result incrementally confirms the hypotheses Prob(h | e) ≈ 2% >. 1%€=€Prob(h). But it does not absolutely confirm the hypothesis as 2% is not ‘high’. Similarly, a positive test result certainly speaks in favor of the hypothesis and therefore supports it but, given these numbers, does not constitute a good reason to infer€it. More generally, under a sensible assignment of probability functions, (a suitable description of) most facts that support a hypothesis will also raise its probability: (an evidential statement describing) fingerprints on the murder weapon will raise the probability of the hypothesis that whoever left the fingerprints was the murderer, (an evidential statement describing) symptoms will raise the probability of the hypothesis that the disease is present and so on.

Footnote omitted] This way formulated, the principle would hardly be practicable. There is no way to take all known facts into account when evaluating a hypothesis, neither for an individual researcher nor for a scientific community. Thus, Carnap quickly appends the principle by stating that irrelevant additional items of evidence may be omitted (139). So we need criteria of evidentiary relevance: criteria that tell us what kinds of facts we have to collect in order to assess a given hypothesis.

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