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Title Causality in the sciences / edited by Phyllis McKay Illari, Frederica Russo, Jon Williamson.
Imprint Oxford : Oxford University Press, 2011.

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Subject Science -- Philosophy.
Epidemiologic Factors.
Public Health.
Quality of Health Care.
Health Care Quality, Access, and Evaluation.
Environment and Public Health.
Delivery of Health Care.
Alt Name Illari, Phyllis McKay,
Russo, Federica, 1978-
Williamson, Jon,
Description 1 online resource (xiii, 938 pages) : illustrations
polychrome rdacc
Bibliography Note Includes bibliographical references and index.
Contents Machine generated contents note: pt. I Introduction -- 1. Why look at causality in the sciences? A manifesto / Jon Williamson -- pt. II Health sciences -- 2. Causality, theories and medicine / R. Paul Thompson -- 3. Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts / Alex Broadbent -- 4. Causal modeling, mechanism, and probability in epidemiology / Harold Kincaid -- 5. The IARC and mechanistic evidence / Erik Weber -- 6. The Russo-Williamson thesis and the question of whether smoking causes heart disease / Donald Gillies -- pt. III Psychology -- 7. Causal thinking / David Lagnado -- 8. When and how do people reason about unobserved causes? / Christian Luhmann -- 9. Counterfactual and generative accounts of causal attribution / Steven A. Sloman -- 10. The autonomy of psychology in the age of neuroscience / Carl Gillett.
11. Turing machines and causal mechanisms in cognitive science / Anna-Mari Rusanen -- 12. Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological research methods / Keith A. Markus -- pt. IV Social sciences -- 13. Causal mechanisms in the social realm / Daniel Little -- 14. Getting past Hume in the philosophy of social science / Ruth Groff -- 15. Causal explanation: Recursive decompositions and mechanisms / Federica Russo -- 16. Counterfactuals and causal structure / Kevin D. Hoover -- 17. The error term and its interpretation in structural models in econometrics / Damien Fennell -- 18. A comprehensive causality test based on the singular spectrum analysis / Abdol S. Soofi -- pt. V Natural sciences -- 19. Mechanism schemas and the relationship between biological theories / Tudor M. Baetu -- 20. Chances and causes in evolutionary biology: How many chances become one chance / Roberta L. Millstein -- 21. Drift and the causes of evolution / Sahotra Sarkar -- 22. In defense of a causal requirement on explanation / Garrett Pendergraft -- 23. Epistemological issues raised by research on climate change / Paolo Vineis -- 24. Explicating the notion of 'causation' : the role of extensive quantities / Antonio Saggion -- 25. Causal completeness of probability theories -- results and open problems / Balaza Gyenis -- pt. VI Computer science, probability, and statistics -- 26. Causality workbench / Alexander Statnikov -- 27. When are graphical causal models not good models? / Abdellah Touhafi -- 28. Why making Bayesian networks objectively Bayesian makes sense / Dawn E. Holmes.
29. Probabilistic measures of causal strength / Christopher Hitchcock -- 30. A new causal power theory / Lucas Hope -- . 31 Multiple testing of causal hypotheses / Bud Mishra -- 32. Measuring latent causal structure / Ricardo Silva -- 33. The structural theory of causation / Judea Pearl -- 34. Defining and identifying the effect of treatment on the treated / A. Philip Dawid -- 35. Predicting 'it will work for us' : (way) beyond statistics / Nancy Cartwright -- pt. VII Causality and mechanisms -- 36. The idea of mechanism / Stathis Psillos -- 37. Singular and general causal relations : a mechanist perspective / Stuart Glennan -- 38. Mechanisms are real and local / Jon Williamson -- 39. Mechanistic information and causal continuity / Peter Machamer -- 40. The causal-process-model theory of mechanisms / Phil Dowe -- 41. Mechanisms in dynamically complex systems / Meinard Kuhlmann -- 42. Third time's a charm : Causation, science and Wittgensteinian pluralism / Julian Reiss.
Summary Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.
Note English.
Print version record.
ISBN 9780191728921 (ebook)
0191728926 (ebook)
OCLC # 755637241
Additional Format Causality in the sciences. Oxford : Oxford University Press, 2011 9780199574131 (DLC) 2011922687 (OCoLC)694832825

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