By Julian Reiss
In this ebook, Reiss argues in want of a good healthy among facts, idea and goal in our causal investigations within the sciences. there is not any doubt that the sciences hire an unlimited array of concepts to deal with causal questions equivalent to managed experiments, randomized trials, statistical and econometric instruments, causal modeling and concept experiments. yet how do those varied tools relate to one another and to the causal inquiry to hand? Reiss argues that there's no "gold common" in settling causal concerns opposed to which different equipment will be measured. quite, some of the tools of inference are typically sturdy simply relative to convinced interpretations of the notice "cause", and every interpretation, in flip, is helping to deal with a few salient objective (prediction, clarification or coverage research) yet now not others. the most goal of this publication is to discover the metaphysical and methodological outcomes of this view within the context of diverse instances experiences from the average and social sciences.
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Extra info for Causation, Evidence, and Inference
AÂ€description can be found in any econometrics textbook. There are many forms of causal pluralism. We can be pluralists about the epistemology of causation and believe that more than one type of evidence can support causal claims. We can believe that there are various causal concepts within one theoretical framework such as when positive and negative causes are defined within a probabilistic framework or causes and preventers within a counterfactual framework. We can be semantic pluralists by maintaining that there is no one privileged framework.
For all claims, whether causal or not, we can ask from what claims they can be inferred and what claims we can infer from them. AÂ€statement such as the first Robbins quote follows deductively from the definition of value. Causal claims follow rarely if ever deductively from claims describing the evidence, and a definition does not describe evidence. We can tell that Robbins’ statement does 26â•… Causation, Evidence, and Inference not express a causal relation by inspecting the system of statements with which it is inferentially related.
Mill’s methods are of course not designed as a theory of evidence. However, they do address our two questions: What kinds of facts do we have to collect in order to support a (in Mill’s as in our case, causal) hypothesis? What constitutes a good reason to believe or act on a hypothesis? Consider for instance the method of difference, which he describes as follows (Mill  1874): If an instance in which the phenomenon under investigation occurs, and an instance in which it does not occur, have every circumstance in common save one, that one occurring only in the former; the circumstance in which alone the two instances differ is the effect, or the cause, or an indispensable part of the cause, of the phenomenon.