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Causality in the Sciences
Edited by Phyllis McKay Illari, Federica Russo, and Jon Williamson
952 pages
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79 line drawings plus 2 colour plates
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234x156mm
978-0-19-957413-1
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Hardback
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17 March 2011
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- Multidisciplinary, providing an accurate account of work on causality across the sciences and philosphy
- Comprehensive, covering many different scientific disciplines
- Cutting-edge, giving readers an insight into the very latest research
- Timely
- Structured according to scientific disciplines, making it easy for readers to find their personal areas of interest
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.Readership: Researchers in the sciences (to learn about
the latest methods for, and controversies surrounding, causal inference), researchers in philosophy (to learn about new ideas on the nature of causality) and graduate students in philosophy and the sciences.
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Edited by Phyllis McKay Illari, Research Fellow, University of Kent, Federica Russo, Research Associate, University of Kent, and Jon Williamson, Professor of Reasoning, Inference and Scientific Method, University of Kent 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.
Contributors:
Woo-kyoung Ahn (Yale University, USA)
Ken Aizawa (Centenary College of Louisiana, USA)
C. Aliferis
Tudor M. Baetu
Jim Bogen (University of Pittsburgh, USA)
Giovanni Boniolo (Università di Milano, Italy)
Alex Broadbent (University of Cambridge, UK)
Nancy Cartwright (London School of Economics, UK)
G. Cooper
Flavio D'Abramo (Imperial College, London, UK)
A. Philip Dawid (University of Cambridge, UK)
Phil Dowe (University of Queensland, Australia)
Rossella Faraldo (University of Padova, Italy)
Damien Fennell (London School
of Economics, UK)
Branden Fitelson (University of California, Berkeley)
Sara Geneletti (Imperial College, London, UK)
Carl Gillet (Northern Illinois University, USA)
Donald Gillies (University College London, UK)
Stuart Glennan (Butler University, USA)
Ruth Groff (St. Louis University, USA)
Isabelle Guyon (ClopiNet, USA)
Balázs Gyenis (University of Pittsburgh, USA)
Hossein Hassani (Cardiff University, UK)
Christopher Hitchcock (California Institute of Technology, USA)
Dawn E. Holmes (University of California, Santa Barbara, USA)
Kevin D. Hoover (Duke University, USA)
Lucas Hope (Bayesian Intelligence Pty. Ltd.)
Aneire Khan (Imperial
College, London, UK)
Harold Kinkaid (University of Alabama, USA)
Samantha Kleinberg (New York University, USA)
Kevin B. Korb (Monash University, Australia)
M. Kuhlmann (University of Bremen, Germany)
David Lagnado (University College London, UK)
Otto Lappi (University of Helsinki, Finland)
Jan Lemeire (Vrije Universiteit Brussel)
Bert Leuridan (University of Ghent, Belgium)
Daniel Little (University of Michigan-Dearborn, USA)
Christian Luhmann (Stony Brook University, USA)
Peter Machamer (University of Pittsburgh, USA)
Keith A. Markus (The City University of New York, USA)
Phyllis McKay Illari (University of Kent, UK)
Roberta L.
Millstein (University of California, Davis)
Bud Mishra (New York University, USA)
Michel Mouchart (University of Louvain, Belgium)
Erik P. Nyberg (University of Melbourne, Australia)
Kerry Patterson (University of Reading, UK)
Judea Pearl (University of California, Los Angeles, USA)
A. Elisseeff J.-P. Pellet
Garrett Pendergraft
Stathis Psillos (University of Athens, Greece)
Miklós Rédei (London School of Economics and Political Science, UK)
Julian Reiss (Erasmus University Rotterdam, The Netherlands)
Benjamin Rottman (Yale University, USA)
Anna-Mari Rusanen (University of Helsinki, Finland)
Federica Russo (University of Kent, UK)
Antonio
Saggion (University of Padova, Italy)
Sahotra Sarkar (University of Texas, Austin, USA)
Ricardo Silva (University College London, UK)
Steven A. Sloman (Brown University, USA)
Abdol S. Soofi (University of Wisconsin-Platteville)
P. Spirtes
A. Statnikov
Kris Steenhaut (Vrije Universiteit Brussel)
R. Paul Thompson (University of Toronto, Canada)
Abdellah Touhafi (Vrije Universiteit Brussel)
Paolo Vineis (Imperial College, London, UK)
Clare R. Walsh (University of Plymouth, UK)
Erik Weber (University of Ghent, Belgium)
Jon Williamson (University of Kent, UK)
Anatoly Zhigljavsky (Cardiff University, UK)
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"This volume is a highly welcome addition to the current discussion of causality at the intersection of philosophy and the sciences ... the volume provides a well-chosen compilation of contributions proving the ongoing progress in exploring the nature of causality and in developing applicable causal concepts in cooperation of philosophy and science. We can warmly recommend Causality in the Sciences to any philosophically interested scientist or philosopher interested in causality (or mechanisms) and its (their) applications in the sciences." - Metascience
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PART I - Introduction
1: Phyllis McKay Illari, Federica Russo, Jon Williamson: Why look at Causality in the Sciences?
PART II - Health Sciences
2: R. Paul Thompson: Causality, Theories, and Medicine
3: Alex Broadbent: Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts
4: Harold Kinkaid: Causal Modeling, Mechanism, and Probability in Epidemiology
5: Bert Leuridan, Erik Weber: The IARC and Mechanistic Evidence
6: Donald Gillies: The Russo-Williamson Thesis and the Question of whether Smoking Causes Heart Disease
PART III - Psychology
7: David Lagnado: Causal Thinking
8: 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 Attribution
10: Ken Aizawa, Carl Gillet: The Autonomy of Psychology in the Age of Neuroscience
11: Otto Lappi, Anna-Mari Rusanen: Turing Machines and Causal Mechanisms in Cognitive Science
12: Keith A. Markus: 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: Daniel Little: Causal Mechanisms in the Social Realm
14: Ruth Groff: Getting Past Hume in the Philosophy of Social Science
15: Michel Mouchart, Federica Russo: Causal Explanation: Recursive Decompositions and Mechanisms
16: Kevin D. Hoover: Counterfactuals and Causal Structure
17: Damien Fennell: The Error Term and its Interpretation in Structural Models in Econometrics
18: Hossein Hassani, Anatoly Zhigljavsky, Kerry Patterson, Abdol S. Soofi: A Comprehensive Causality Test Based on the Singular Spectrum Analysis
PART V - Natural Sciences
19: Tudor M. Baetu: Mechanism Schemas and the Relationship Between Biological Theories
20: Roberta L. Millstein: Chances and Causes in Evolutionary Biology: How Many Chances Become One Chance
21: Sahotra Sarkar: Drift and the Causes of Evolution
22: Garrett Pendergraft: In Defense of a Causal Requirement on Explanation
23: Paolo Vineis, Aneire Khan, Flavio D'Abramo: Epistemological Issues Raised by Research on Climate Change
24: Giovanni Boniolo, Rossella Faraldo, Antonio Saggion: Explicating the Notion of 'Causation': the Role of the Extensive Quantities
25: Miklos Redei, Balazs Gyenis: Causal Completeness of Probability Theories-results and Open Problems
PART VI - Computer Science, Probability, and Statistics
26: Isabelle Guyon, C. Aliferis, G. Cooper, A. Elisseeff J.-P. Pellet, P. Spirtes, A. Statnikov: Causality Workbench
27: Jan Lemeire, Kris Steenhaut, Abdellah Touhafi: When are Graphical Models not Good Models
28: Dawn E. Holmes: Why Making Bayesian Networks Objectively Bayesian Make Sense
29: Branden Fitelson, Christopher Hitchcock: Probabilistic Measures of Causal Strength
30: Kevin B Korb, Erik P. Nyberg, Lucas Hope: A New Causal Power Theory
31: Samantha Kleinberg, Bud Mishra: Multiple Testing of Causal Hypotheses
32: Ricardo Silva: Measuring Latent Causal Structure
33: Judea Pearl: The Structural Theory of Causation
34: Sara Geneletti, A. Philip Dawid: Defining and Identifying the Effect of Treatment on the Treated
35: Nancy Cartwright: Predicting 'It Will Work for Us': (Way) Beyond Statistics
PART VII - Causality and Mechanisms
36: Stathis Psillos: The Idea of Mechanism
37: Stuart Glennan: Singular and General Causal Relations: A Mechanist Perspective
38: Phyllis McKay Illari, Jon Williamson: Mechanisms are Real and Local
39: Jim Bogen, Peter Machamer: Mechanistic Information and Causal Continuity
40: Phil Dowe: The Causal-Process-Model Theory of Mechanisms
41: M. Kuhlmann: Mechanisms in Dynamically Complex Systems
42: Julian Reiss: Third Time's a Charm: Causation, Science, and Wittgensteinian Pluralism
Index
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