OXFORD UNIVERSITY PRESS

Causality in the Sciences

ISBN : 9780199574131

Price(incl.tax): 
¥12,419
Author: 
Phyllis McKay Illari; Federica Russo; Jon Williamson
Pages
952 Pages
Format
Hardcover
Size
163 x 240 mm
Pub date
Mar 2011
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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, 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.

Index: 

PART I - INTRODUCTION
1. Why look at Causality in the Sciences?
PART II - HEALTH SCIENCES
2. Causality, Theories, and Medicine
3. Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts
4. Causal Modeling, Mechanism, and Probability in Epidemiology
5. The IARC and Mechanistic Evidence
6. The Russo-Williamson Thesis and the Question of whether Smoking Causes Heart Disease
PART III - PSYCHOLOGY
7. Causal Thinking
8. When and How Do People Reason about Unobserved Causes?
9. Counterfactual and Generative Accounts of Causal Attribution
10. The Autonomy of Psychology in the Age of Neuroscience
11. Turing Machines and Causal Mechanisms in Cognitive Science
12. Real Causes and Ideal Manipulations: Pearl's Theory of Causal Inference from the Point of View of Psychological Research Methods
PART IV - SOCIAL SCIENCES
13. Causal Mechanisms in the Social Realm
14. Getting Past Hume in the Philosophy of Social Science
15. Causal Explanation: Recursive Decompositions and Mechanisms
16. Counterfactuals and Causal Structure
17. The Error Term and its Interpretation in Structural Models in Econometrics
18. A Comprehensive Causality Test Based on the Singular Spectrum Analysis
PART V - NATURAL SCIENCES
19. Mechanism Schemas and the Relationship Between Biological Theories
20. Chances and Causes in Evolutionary Biology: How Many Chances Become One Chance
21. Drift and the Causes of Evolution
22. In Defense of a Causal Requirement on Explanation
23. Epistemological Issues Raised by Research on Climate Change
24. Explicating the Notion of 'Causation': the Role of the Extensive Quantities
25. Causal Completeness of Probability Theories-results and Open Problems
PART VI - COMPUTER SCIENCE, PROBABILITY, AND STATISTICS
26. Causality Workbench
27. When are Graphical Models not Good Models
28. Why Making Bayesian Networks Objectively Bayesian Make Sense
29. Probabilistic Measures of Causal Strength
30. A New Causal Power Theory
31. Multiple Testing of Causal Hypotheses
32. Measuring Latent Causal Structure
33. The Structural Theory of Causation
34. Defining and Identifying the Effect of Treatment on the Treated
35. Predicting 'It Will Work for Us': (Way) Beyond Statistics
PART VII - CAUSALITY AND MECHANISMS
36. The Idea of Mechanism
37. Singular and General Causal Relations: A Mechanist Perspective
38. Mechanisms are Real and Local
39. Mechanistic Information and Causal Continuity
40. The Causal-Process-Model Theory of Mechanisms
41. Mechanisms in Dynamically Complex Systems
42. Third Time's a Charm: Causation, Science, and Wittgensteinian Pluralism
Index

About the author: 

Phyllis 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 the sciences that uses understanding of the discovery and use of causal mechanisms in different sciences to inform philosophical work on causality. ; Federica Russo is currently Research Associate at the University of Kent and has visited the Centre for Philosophy of Natural and Social Science (CPNSS) at the LSE from April 2004 to January 2005 and the Center for Philosophy of Science (Pittsburgh) from January to April 2009. She is interested in causality and probability in the social, biomedical and policy sciences, as well as in the philosophical, legal, and social, implications of technology. Federica is part of the editorial board of the journal Philosophy and Technology and features editor of the monthly gazette The Reasoner. ; Jon Williamson is Professor of Reasoning, Inference and Scientific Method in the philosophy department at the University of Kent. He works on causality, probability, logic and applications of formal reasoning within science, mathematics and artificial intelligence. Jon currently heads the philosophy department and is a director of the multi-disciplinary University of Kent Centre for Reasoning. He runs the Reasoning Club, a network of research centres, and edits The Reasoner, a monthly gazette on research in this area. Jon was Times Higher Education UK Young Researcher of the Year 2007.

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