Oxford University Press, 2011. — 952 p. — ISBN: 0199574138.There is a need for integrated thinking about causality, probability and mechanisms in scientific methodology. Causality and probability are long-established central concepts in the sciences, with a corresponding philosophical literature examining their problems. On the other hand, the philosophical literature examining mechanisms is not long-established, and there is no clear idea of how mechanisms relate to causality and probability. But we need some idea if we are to understand causal inference in the sciences: a panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, routinely make use of probability, statistics, theory and mechanisms to infer causal relationships. These disciplines have developed very different methods, where causality and probability often seem to have different understandings, and where the mechanisms involved often look very different. This variegated situation raises the question of whether the different sciences are really using different concepts, or whether progress in understanding the tools of causal inference in some sciences can lead to progress in other sciences. The book tackles these questions as well as others concerning the use of causality in the sciences.Introduction Why look at causality in the sciences? A manifesto Health sciences Causality, theories and medicine Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts Causal modelling, mechanism, and probability in epidemiology The IARC and mechanistic evidence The Russo–Williamson thesis and the question of whether smoking causes heart disease Psychology Causal thinking When and how do people reason about unobserved causes? Counterfactual and generative accounts of causal attribution The autonomy of psychology in the age of neuroscience Turing machines and causal mechanisms in cognitive science Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological research methods Social sciences Causal mechanisms in the social realm Getting past Hume in the philosophy of social science Causal explanation: Recursive decompositions and mechanisms Counterfactuals and causal structure The error term and its interpretation in structural models in econometrics A comprehensive causality test based on the singular spectrum analysis Natural sciences Mechanism schemas and the relationship between biological theories Chances and causes in evolutionary biology: How many chances become one chance Drift and the causes of evolution In defense of a causal requirement on explanation Epistemological issues raised by research on climate change Explicating the notion of ‘causation’: The role of extensive quantities Causal completeness of probability theories — Results and open problems Computer science, probability, and statistics Causality Workbench When are graphical causal models not good models? Why making Bayesian networks objectively Bayesian makes sense Probabilistic measures of causal strength A new causal power theory Multiple testing of causal hypotheses Measuring latent causal structure The structural theory of causation Defining and identifying the effect of treatment on the treated Predicting ‘It will work for us’: (Way) beyond statistics Causality and mechanisms The idea of mechanism Singular and general causal relations: A mechanist perspective Mechanisms are real and local Mechanistic information and causal continuity The causal‐process‐model theory of mechanisms Mechanisms in dynamically complex systems Third time's a charm: Causation, science and Wittgensteinian pluralism
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