Causality in the Sciences

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.

Table of Contents:
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

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.

Review :
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.