Causality in the Sciences

Hardcover | April 10, 2011

EditorPhyllis McKay Illari, Federica Russo, Jon Williamson

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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, thephilosophical 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 usingdifferent 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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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, thephilosophical literature examining mechanisms is n...

Phyllis McKay Illari is currently a postdoctoral researcher at the University of Kent. She has also held posts at the Universities of Stirling and Bristol. She is interested in all aspects of the metaphysics and methodology of causality. She is currently working on a Leverhulme-Trust funded project on mechanisms and causality across th...

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Format:HardcoverDimensions:952 pages, 9.21 × 6.14 × 0.1 inPublished:April 10, 2011Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199574138

ISBN - 13:9780199574131

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Table of Contents

Part I - Introduction1. Phyllis McKay Illari, Federica Russo, Jon Williamson: Why look at Causality in the Sciences?Part II - Health Sciences2. R. Paul Thompson: Causality, Theories, and Medicine3. Alex Broadbent: Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts4. Harold Kinkaid: Causal Modeling, Mechanism, and Probability in Epidemiology5. Bert Leuridan, Erik Weber: The IARC and Mechanistic Evidence6. Donald Gillies: The Russo-Williamson Thesis and the Question of whether Smoking Causes Heart DiseasePart III - Psychology7. David Lagnado: Causal Thinking8. Benjamin Rottman, Woo-kyoung Ahn, Christian Luhmann: When and How Do People Reason about Unobserved Causes?9. Clare R Walsh, Steven A Sloman: Counterfactual and Generative Accounts of Causal Attribution10. Ken Aizawa, Carl Gillet: The Autonomy of Psychology in the Age of Neuroscience11. Otto Lappi, Anna-Mari Rusanen: Turing Machines and Causal Mechanisms in Cognitive Science12. Keith A. Markus: Real Causes and Ideal Manipulations: Pearl's Theory of Causal Inference from the Point of View of Psychological Research MethodsPart IV - Social Sciences13. Daniel Little: Causal Mechanisms in the Social Realm14. Ruth Groff: Getting Past Hume in the Philosophy of Social Science15. Michel Mouchart, Federica Russo: Causal Explanation: Recursive Decompositions and Mechanisms16. Kevin D. Hoover: Counterfactuals and Causal Structure17. Damien Fennell: The Error Term and its Interpretation in Structural Models in Econometrics18. Hossein Hassani, Anatoly Zhigljavsky, Kerry Patterson, Abdol S. Soofi: A Comprehensive Causality Test Based on the Singular Spectrum AnalysisPart V - Natural Sciences19. Tudor M. Baetu: Mechanism Schemas and the Relationship Between Biological Theories20. Roberta L. Millstein: Chances and Causes in Evolutionary Biology: How Many Chances Become One Chance21. Sahotra Sarkar: Drift and the Causes of Evolution22. Garrett Pendergraft: In Defense of a Causal Requirement on Explanation23. Paolo Vineis, Aneire Khan, Flavio D'Abramo: Epistemological Issues Raised by Research on Climate Change24. Giovanni Boniolo, Rossella Faraldo, Antonio Saggion: Explicating the Notion of 'Causation': the Role of the Extensive Quantities25. Miklos Redei, Balazs Gyenis: Causal Completeness of Probability Theories-results and Open ProblemsPart VI - Computer Science, Probability, and Statistics26. Isabelle Guyon, C. Aliferis, G. Cooper, A. Elisseeff J.-P. Pellet, P. Spirtes, A. Statnikov: Causality Workbench27. Jan Lemeire, Kris Steenhaut, Abdellah Touhafi: When are Graphical Models not Good Models28. Dawn E. Holmes: Why Making Bayesian Networks Objectively Bayesian Make Sense29. Branden Fitelson, Christopher Hitchcock: Probabilistic Measures of Causal Strength30. Kevin B Korb, Erik P. Nyberg, Lucas Hope: A New Causal Power Theory31. Samantha Kleinberg, Bud Mishra: Multiple Testing of Causal Hypotheses32. Ricardo Silva: Measuring Latent Causal Structure33. Judea Pearl: The Structural Theory of Causation34. Sara Geneletti, A. Philip Dawid: Defining and Identifying the Effect of Treatment on the Treated35. Nancy Cartwright: Predicting 'It Will Work for Us': (Way) Beyond StatisticsPart VII - Causality and Mechanisms36. Stathis Psillos: The Idea of Mechanism37. Stuart Glennan: Singular and General Causal Relations: A Mechanist Perspective38. Phyllis McKay Illari, Jon Williamson: Mechanisms are Real and Local39. Jim Bogen, Peter Machamer: Mechanistic Information and Causal Continuity40. Phil Dowe: The Causal-Process-Model Theory of Mechanisms41. M. Kuhlmann: Mechanisms in Dynamically Complex Systems42. Julian Reiss: Third Time's a Charm: Causation, Science, and Wittgensteinian PluralismIndex