Causality in the Sciences 1st Edition by Phyllis McKay Illari, Federica Russo, Jon Williamson 0199574138 9780199574131

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ISBN 10: 0199574138 

ISBN 13: 9780199574131

Author: Phyllis McKay Illari, Federica Russo, Jon Williamson

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 Why look at causality in the sciences? A manifesto

Part II: Health sciences

2 Causality, theories and medicine

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

4 Causal modelling, mechanism, and probability in epidemiology

5 The IARC and mechanistic evidence

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

Part III: Psychology

7 Causal thinking

8 When and how do people reason about unobserved causes?

9 Counterfactual and generative accounts of causal attribution

10 The autonomy of psychology in the age of neuroscience

11 Turing machines and causal mechanisms in cognitive science

12 Real causes and ideal manipulations: Pearl’s theory of causal inference from the point of view of

Part IV: Social sciences

13 Causal mechanisms in the social realm

14 Getting past Hume in the philosophy of social science

15 Causal explanation: Recursive decompositions and mechanisms

16 Counterfactuals and causal structure

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

18 A comprehensive causality test based on the singular spectrum analysis

Part V: Natural sciences

19 Mechanism schemas and the relationship between biological theories

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

21 Drift and the causes of evolution

22 In defense of a causal requirement on explanation

23 Epistemological issues raised by research on climate change

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

25 Causal completeness of probability theories – Results and open problems

Part VI: Computer science, probability, and statistics

26 Causality Workbench

27 When are graphical causal models not good models?

28 Why making Bayesian networks objectively Bayesian makes sense

29 Probabilistic measures of causal strength

30 A new causal power theory

31 Multiple testing of causal hypotheses

32 Measuring latent causal structure

33 The structural theory of causation

34 Defining and identifying the effect of treatment on the treated

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

Part VII: Causality and mechanisms

36 The idea of mechanism

37 Singular and general causal relations: A mechanist perspective

38 Mechanisms are real and local

39 Mechanistic information and causal continuity

40 The causal-process-model theory of mechanisms

41 Mechanisms in dynamically complex systems

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

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Tags: Phyllis McKay Illari, Federica Russo, Jon Williamson, Causality