Causality in the Sciences
Phyllis McKay Illari, Federica Russo, Jon Williamson
Oxford University Press, Mar 17, 2011 - Mathematics - 938 pages
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.
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aflatoxin algorithm analysis approach argue assumptions Bayesian networks behaviour biology Cambridge cancer carcinogenicity Cartwright causal claims causal effect causal inference causal models causal power causal processes causal reasoning causal relations causal relationships causal strength causal structure chance classical genetics cognitive common cause computational concept conditional independence consider context correlation counterfactual covariation Craver d-separates defined depends discussion distribution dynamical epidemiology equations equilibrium explanations error term evidence evolutionary example experimental explanatory extensive quantity factors forecasting function genetics Granger causality graph hypothesis IARC identified independent individual instance interaction interpretation intervention latent variables laws lung cancer measure mechanistic explanation metaphysics methods molecular multiple realization notion observed outcome particular Pearl Philosophy of Science physical population possible prediction probabilistic probability probability space problem properties random realizers relevant requires role Section statistical studies theory tion Turing machines University Press unobserved causes Woodward