Causality in the Sciences

by Illari, Phyllis McKay; Russo, Federica; Williamson, Jon

• ISBN13: 9780199574131
• ISBN10: 0199574138
• Format: Hardcover
• Copyright: 2011-05-12
• Publisher: Oxford University Press

Summary

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 understandcausal 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 concerningthe use of causality in the sciences.

Author Biography

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 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.

Table of Contents

List of Contributors	p. ix
Introduction	p. 1
Why look at causality in the sciences? A manifesto	p. 3
Health sciences	p. 23
Causality, theories and medicine	p. 25
Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts	p. 45
Causal modelling, mechanism, and probability in epidemiology	p. 70
The IARC and mechanistic evidence	p. 91
The Russo-Williamson thesis and the question of whether smoking causes heart disease	p. 110
Psychology	p. 127
Causal thinking	p. 129
When and how do people reason about unobserved causes?	p. 150
Counterfactual and generative accounts of causal attribution	p. 184
The autonomy of psychology in the age of neuroscience	p. 202
Turing machines and causal mechanisms in cognitive science	p. 224
Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological research methods	p. 240
Social sciences	p. 271
Causal mechanisms in the social realm	p. 273
Getting past Hume in the philosophy of social science	p. 296
Causal explanation: Recursive decompositions and mechanisms	p. 317
Counterfactuals and causal structure	p. 338
The error term and its interpretation in structural models in econometrics	p. 361
A comprehensive causality test based on the singular spectrum analysis	p. 379
Natural sciences	p. 405
Mechanism schemas and the relationship between biological theories	p. 407
Chances and causes in evolutionary biology: How many chances become one chance	p. 425
Drift and the causes of evolution	p. 445
In defense of a causal requirement on explanation	p. 470
Epistemological issues raised by research on climate change	p. 493
Explicating the notion of 'causation': The role of extensive quantities	p. 502
Causal completeness of probability theories - Results and open problems	p. 526
Computer science, probability, and statistics	p. 541
Causality Workbench	p. 543
When are graphical causal models not good models?	p. 562
Why making Bayesian networks objectively Bayesian makes sense	p. 583
Probabilistic measures of causal strength	p. 600
A new causal power theory	p. 628
Multiple testing of causal hypotheses	p. 653
Measuring latent causal structure	p. 673
The structural theory of causation	p. 697
Defining and identifying the effect of treatment on the treated	p. 728
Predicting'It will work for us': (Way) beyond statistics	p. 750
Causality and mechanisms	p. 769
The idea of mechanism	p. 771
Singular and general causal relations: A mechanist perspective	p. 789
Mechanisms are real and local	p. 818
Mechanistic information and causal continuity	p. 845
The causal-process-model theory of mechanisms	p. 865
Mechanisms in dynamically complex systems	p. 880
Third time's a charm: Causation, science and Wittgensteinian pluralism	p. 907
Index	p. 929
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