Muutke küpsiste eelistusi

E-raamat: Causality in the Sciences

Edited by (Professor of Reasoning, Inference and Scientific Method, University of Kent), Edited by (Research Associate, University of Kent), Edited by (Research Fellow, University of Kent)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 17-Mar-2011
  • Kirjastus: Oxford University Press
  • Keel: eng
  • ISBN-13: 9780191060328
  • Formaat - PDF+DRM
  • Hind: 119,44 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
    • Oxford Scholarship Online e-raamatud
  • Formaat: PDF+DRM
  • Ilmumisaeg: 17-Mar-2011
  • Kirjastus: Oxford University Press
  • Keel: eng
  • ISBN-13: 9780191060328

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.

Arvustused

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. * Metascience *

List of Contributors
ix
Part I Introduction
1(22)
1 Why look at causality in the sciences? A manifesto
3(20)
Phyllis McKay Illari
Federica Russo
Jon Williamson
Part II Health sciences
23(104)
2 Causality, theories and medicine
25(20)
R. Paul Thompson
3 Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts
45(25)
Alex Broadbent
4 Causal modelling, mechanism, and probability in epidemiology
70(21)
Harold Kincaid
5 The IARC and mechanistic evidence
91(19)
Bert Leuridan
Erik Weber
6 The Russo-Williamson thesis and the question of whether smoking causes heart disease
110(17)
Donald Gillies
Part III Psychology
127(144)
7 Causal thinking
129(21)
David Lagnado
8 When and how do people reason about unobserved causes?
150(34)
Benjamin Rottman
Woo-Kyoung Ahn
Christian Luhmann
9 Counterfactual and generative accounts of causal attribution
184(18)
Clare R. Walsh
Steven A. Sloman
10 The autonomy of psychology in the age of neuroscience
202(22)
Ken Aizawa
Carl Gillett
11 Turing machines and causal mechanisms in cognitive science
224(16)
Otto Lappi
Anna-Mari Rusanen
12 Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological research methods
240(31)
Keith A. Markus
Part IV Social sciences
271(134)
13 Causal mechanisms in the social realm
273(23)
Daniel Little
14 Getting past Hume in the philosophy of social science
296(21)
Ruth Groff
15 Causal explanation: Recursive decompositions and mechanisms
317(21)
Michel Mouchart
Federica Russo
16 Counterfactuals and causal structure
338(23)
Kevin D. Hoover
17 The error term and its interpretation in structural models in econometrics
361(18)
Damien Fennell
18 A comprehensive causality test based on the singular spectrum analysis
379(26)
Hossein Hassani
Anatoly Zhigljavsky
Kerry Patterson
Abdol S. Soofi
Part V Natural sciences
405(136)
19 Mechanism schemas and the relationship between biological theories
407(18)
Tudor M. Baetu
20 Chances and causes in evolutionary biology: How many chances become one chance
425(20)
Roberta L. Millstein
21 Drift and the causes of evolution
445(25)
Sahotra Sarkar
22 In defense of a causal requirement on explanation
470(23)
Garrett Pendergraft
23 Epistemological issues raised by research on climate change
493(9)
Paolo Vineis
Aneire Khan
Flavio D'Abramo
24 Explicating the notion of `causation': The role of extensive quantities
502(24)
Giovanni Boniolo
Rossella Faraldo
Antonio Saggion
25 Causal completeness of probability theories - Results and open problems
526(15)
Miklos Redei
Baldzs Gyenis
Part VI Computer science, probability, and statistics
541(228)
26 Causality Workbench
543(19)
Isabelle Guyon
Constantin Aliferis
Gregory Cooper
Andre Elisseeff
Jean-Philippe Pellet
Peter Spirtes
Alexander Statnikov
27 When are graphical causal models not good models?
562(21)
Jan Lemeire
Kris Steenhaut
Abdellah Touhafi
28 Why making Bayesian networks objectively Bayesian makes sense
583(17)
Dawn E. Holmes
29 Probabilistic measures of causal strength
600(28)
Branden Fitelson
Christopher Hitchcock
30 A new causal power theory
628(25)
Kevin B. Korb
Erik P. Nyberg
Lucas Hope
31 Multiple testing of causal hypotheses
653(20)
Samantha Kleinberg
Bud Mishra
32 Measuring latent causal structure
673(24)
Ricardo Silva
33 The structural theory of causation
697(31)
Judea Pearl
34 Defining and identifying the effect of treatment on the treated
728(22)
Sara Geneletti
A. Philip Dawid
35 Predicting `It will work for us': (Way) beyond statistics
750(19)
Nancy Cartwright
Part VII Causality and mechanisms
769(160)
36 The idea of mechanism
771(18)
Stathis Psillos
37 Singular and general causal relations: A mechanist perspective
789(29)
Stuart Glennan
38 Mechanisms are real and local
818(27)
Phyllis McKay Illari
Jon Williamson
39 Mechanistic information and causal continuity
845(20)
Jim Bogen
Peter Machamer
40 The causal-process-model theory of mechanisms
865(15)
Phil Dowe
41 Mechanisms in dynamically complex systems
880(27)
Meinard Kuhlmann
42 Third time's a charm: Causation, science and Wittgensteinian pluralism
907(22)
Julian Reiss
Index 929
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.