Causation Evidence and Inference (2015)英文清晰.pdf

Causation, Evidence, and Inference In this book, Reiss argues in favor of a tight fit between evidence, con- cept, and purpose in our causal investigations in the sciences. There is no doubt that the sciences employ a vast array of techniques to address causal questions such as controlled experiments, randomized trials, statistical and econometric tools, causal modeling and thought experiments. But how do these different methods relate to each other and to the causal inquiry at hand? Reiss argues that there is no 鈥榞old standard鈥 in settling causal issues against which other methods can be measured. Rather, the various methods of inference tend to be good only relative to certain interpretations of the word 鈥榗ause鈥? and each interpretation, in turn, helps to address some salient purpose (e.g., prediction, explanation, or policy analysis) but not others. The main objective of this book is to explore the metaphysical and method- ological consequences of this view in the context of numerous cases studies from the natural and social sciences. Julian Reiss (PhD 2002, LSE) is Professor of Philosophy at Durham Uni- versity and Co-Director of the Centre for Humanities Engaging Science and Society (CHESS). His main research interests are methodologies of the sci- ences (especially causality and causal inference, models, simulations and thought experiments, and counterfactuals), philosophy of economics, and science and values. Routledge Studies in the Philosophy of Science 1 Evolution, Rationality and 9 Models, Simulations, and Cognition Representations A Cognitive Science for the Paul Humphreys and Cyrille Twenty-First Century Imbert Edited by Ant贸nio Zilh茫o 10 Platonism, Naturalism, and 2 Conceptual Systems Mathematical Knowledge Harold I. Brown