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Causality in the Sciences

Phyllis McKay Illari, Federica Russo, and Jon Williamson


There is a need for integrated thinking about causality, probability, and mechanism in scientific methodology. A panoply of disciplines, ranging from epidemiology and biology through to econometrics and physics, routinely make use of these concepts to infer causal relationships. But each of these disciplines has developed its own 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 progress in understanding the tools of causal inference in some ... More

Keywords: causality, causal inference, probability, mechanism, philosophy of science, epistemology, metaphysics

Bibliographic Information

Print publication date: 2011 Print ISBN-13: 9780199574131
Published to Oxford Scholarship Online: September 2011 DOI:10.1093/acprof:oso/9780199574131.001.0001


Affiliations are at time of print publication.

Phyllis McKay Illari, editor
Research Fellow, University of Kent
Author Webpage

Federica Russo, editor
Research Associate, University of Kent
Author Webpage

Jon Williamson, editor
Professor of Reasoning, Inference and Scientific Method, University of Kent
Author Webpage

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Part I Introduction

1 Why look at causality in the sciences? A manifesto

Phyllis McKay Illari, Federica Russo, and Jon Williamson

Part II Health sciences

5 The IARC and mechanistic evidence

Bert Leuridan and Erik Weber

Part III Psychology

7 Causal thinking

David Lagnado

8 When and how do people reason about unobserved causes?

Benjamin Rottman, Woo‐kyoung Ahn, and Christian Luhmann

PART IV Social sciences

18 A comprehensive causality test based on the singular spectrum analysis

Hossein Hassani, Anatoly Zhigljavsky, Kerry Patterson, and Abdol S. Soofi

PART V Natural sciences

23 Epistemological issues raised by research on climate change

Paolo Vineis, Aneire Khan, and Flavio D'sAbramo

24 Explicating the notion of ‘causation’: The role of extensive quantities

Giovanni Boniolo, Rossella Faraldo, and Antonio Saggion

PART VI Computer science, probability, and statistics

26 Causality Workbench

Isabelle Guyon, Constantin Aliferis, Gregory Cooper, André Elisseeff, Jean‐Philippe Pellet, Peter Spirtes, and Alexander Statnikov

27 When are graphical causal models not good models?

Jan Lemeire, Kris Steenhaut, and Abdellah Touhafi

29 Probabilistic measures of causal strength

Branden Fitelson and Christopher Hitchcock

30 A new causal power theory

Kevin B. Korb,Erik P. Nyberg, and Lucas Hope

31 Multiple testing of causal hypotheses

Samantha Kleinberg and Bud Mishra

PART VII Causality and mechanisms

36 The idea of mechanism

Stathis Psillos

38 Mechanisms are real and local

Phyllis McKay Illari and Jon Williamson

End Matter