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Illari P., Russo F., Williamson J. Causality in the Sciences

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Illari P., Russo F., Williamson J. Causality in the Sciences
Oxford University Press, 2011. — 952 p. — ISBN: 0199574138.
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.
Introduction
Why look at causality in the sciences? A manifesto
Health sciences
Causality, theories and medicine
Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts
Causal modelling, mechanism, and probability in epidemiology
The IARC and mechanistic evidence
The Russo–Williamson thesis and the question of whether smoking causes heart disease
Psychology
Causal thinking
When and how do people reason about unobserved causes?
Counterfactual and generative accounts of causal attribution
The autonomy of psychology in the age of neuroscience
Turing machines and causal mechanisms in cognitive science
Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of
psychological research methods
Social sciences
Causal mechanisms in the social realm
Getting past Hume in the philosophy of social science
Causal explanation: Recursive decompositions and mechanisms
Counterfactuals and causal structure
The error term and its interpretation in structural models in econometrics
A comprehensive causality test based on the singular spectrum analysis
Natural sciences
Mechanism schemas and the relationship between biological theories
Chances and causes in evolutionary biology: How many chances become one chance
Drift and the causes of evolution
In defense of a causal requirement on explanation
Epistemological issues raised by research on climate change
Explicating the notion of ‘causation’: The role of extensive quantities
Causal completeness of probability theories — Results and open problems
Computer science, probability, and statistics
Causality Workbench
When are graphical causal models not good models?
Why making Bayesian networks objectively Bayesian makes sense
Probabilistic measures of causal strength
A new causal power theory
Multiple testing of causal hypotheses
Measuring latent causal structure
The structural theory of causation
Defining and identifying the effect of treatment on the treated
Predicting ‘It will work for us’: (Way) beyond statistics
Causality and mechanisms
The idea of mechanism
Singular and general causal relations: A mechanist perspective
Mechanisms are real and local
Mechanistic information and causal continuity
The causal‐process‐model theory of mechanisms
Mechanisms in dynamically complex systems
Third time's a charm: Causation, science and Wittgensteinian pluralism
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