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Causality in the Sciences

Phyllis McKay Illari, Federica Russo, and Jon Williamson

Abstract

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

Authors

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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Contents

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Front Matter

Title Pages

List of Contributors

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

2 Causality, theories and medicine

R. Paul Thompson

3 Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts

Alex Broadbent

4 Causal modelling, mechanism, and probability in epidemiology

Harold Kincaid

5 The IARC and mechanistic evidence

Bert Leuridan and Erik Weber

6 The Russo–Williamson thesis and the question of whether smoking causes heart disease

Donald Gillies

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

9 Counterfactual and generative accounts of causal attribution

Clare R. Walsh and Steven A. Sloman

10 The autonomy of psychology in the age of neuroscience

Ken Aizawa and Carl Gillett

11 Turing machines and causal mechanisms in cognitive science

Otto Lappi and Anna‐Mari Rusanen

12 Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological research methods

Keith A. Markus

PART IV Social sciences

13 Causal mechanisms in the social realm

Daniel Little

14 Getting past Hume in the philosophy of social science

Ruth Groff

15 Causal explanation: Recursive decompositions and mechanisms

Michel Mouchart and Federica Russo

16 Counterfactuals and causal structure

Kevin D. Hoover

17 The error term and its interpretation in structural models in econometrics

Damien Fennell

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

19 Mechanism schemas and the relationship between biological theories

Tudor M. Baetu

20 Chances and causes in evolutionary biology: How many chances become one chance

Roberta L. Millstein

21 Drift and the causes of evolution

Sahotra Sarkar

22 In defense of a causal requirement on explanation

Garrett Pendergraft

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

25 Causal completeness of probability theories — Results and open problems

Miklós Rédei and Balázs Gyenis

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

28 Why making Bayesian networks objectively Bayesian makes sense

Dawn E. Holmes

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

32 Measuring latent causal structure

Ricardo Silva

33 The structural theory of causation

Judea Pearl

34 Defining and identifying the effect of treatment on the treated

Sara Geneletti and A. Philip Dawid

35 Predicting ‘It will work for us’: (Way) beyond statistics

Nancy Cartwright

PART VII Causality and mechanisms

36 The idea of mechanism

Stathis Psillos

37 Singular and general causal relations: A mechanist perspective

Stuart Glennan

38 Mechanisms are real and local

Phyllis McKay Illari and Jon Williamson

39 Mechanistic information and causal continuity

Jim Bogen and Peter Machamer

40 The causal‐process‐model theory of mechanisms

Phil Dowe

41 Mechanisms in dynamically complex systems

Meinard Kuhlmann

42 Third time's a charm: Causation, science and Wittgensteinian pluralism

Julian Reiss

End Matter

Index