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Bibliographies thématiques / Modelli causali / Livres

Livres sur le sujet « Modelli causali »

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Auteur : Grafiati

Publié le 10 mars 2023

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1

Quartarone, Jlenia. Causazione e intenzionalità : Modelli di spiegazione causale nella filosofia dell'azione contemporanea. Macerata : Quodlibet, 2008.

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2

Causazione e intenzionalità : Modelli di spiegazione causale nella filosofia dell'azione contemporanea. Macerata : Quodlibet, 2008.

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3

Retherford, Robert D. Statistical models for causal analysis. New York : Wiley, 1993.

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4

Hausman, Daniel M. Causal asymmetries. Cambridge, U.K : Cambridge University Press, 1998.

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5

M, Blalock Hubert, dir. Causal models in experimental designs. New Brunswick [N.J.] : AldineTransaction, 2007.

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6

Davis, James Allan. The logic of causal order. Beverly Hills : Sage Publications, 1985.

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7

The logic of causal order. Beverly Hills : Sage Publications, 1985.

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8

Horn, Werner, Dipl.-Ing. Dr., dir. Causal AI models : Steps toward applications. New York : Hemisphere Pub. Corp., 1990.

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9

Linear causal modeling with structural equations. Boca Raton, FL : Chapman & Hall/CRC, 2009.

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10

Schroeder, Douglas A. Accounting and causal effects : Econometric challenges. New York : Springer, 2010.

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11

M, Blalock Hubert, dir. Causal models in the social sciences. 2^e éd. New York : Aldine Pub. Co., 1985.

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12

1926-, Blalock Hubert M., dir. Causal models in the social sciences. 2^e éd. New York : Aldine Pub. Co., 1985.

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13

Causal models : How people think about the world and its alternatives. New York : Oxford University Press, 2005.

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14

Causal analysis with panel data. Thousand Oaks, Calif : Sage Publications, 1995.

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15

Hautier, Jean-Paul. Convertisseurs statiques : Méthodologie causale de modélisation et de commande. Paris : Editions Technip, 1999.

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16

1926-, Blalock Hubert M., dir. Causal models in panel and experimental designs. New York : Aldine Pub. Co., 1985.

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17

M, Blalock Hubert, dir. Causal models in panel and experimental designs. New York : Aldine Pub. Co., 1985.

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18

Stinebrickner, Todd R. The causal effect of studying on academic performance. Cambridge, Mass : National Bureau of Economic Research, 2007.

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19

Moffatt, Ian. Causal and simulation modelling using system dynamics. Norwich : Order from Environmental Publications, University of East Anglia, 1991.

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20

Sheffrin, Steven M. A new approach to causality and economic growth. Boston : Federal Reserve Bank of Boston, 1995.

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21

1968-, Brown Jeffrey R., et National Bureau of Economic Research., dir. Neighbors matter : Causal community effects and stock market participation. Cambridge, Mass : National Bureau of Economic Research, 2007.

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22

Dennis, Sales Bruce, Becker Judith V et American Psychological Association, dir. Sex offending : Causal theories to inform research, prevention, and treatment. Washington, DC : American Psychological Association, 2008.

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23

Levine, Ross. Financial intermediation and growth : Causality and causes. Washington, DC : World Bank, Development Research Group, Macroeconomics and Growth, 1999.

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24

Angrist, Joshua David. Semiparametric causality tests using the policy propensity score. Cambridge, MA : National Bureau of Economic Research, 2004.

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25

Angrist, Joshua David. Semiparametric causality tests using the policy propensity score. Cambridge, Mass : National Bureau of Economic Research, 2004.

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26

Friedman, Benjamin M. Another look at the evidence on money-income causality. Cambridge, MA (1050 Massachusetts Avenue, Cambridge, MA 02138) : National Bureau of Economic Research, 1991.

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27

Young, Gerald. Development and causality : Neo-Piagetian perspectives. New York : Springer, 2011.

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28

J, Thomas J. The links between structural adjustment and poverty : Causal or remedial ? [Santiago, Chile] : PREALC, 1993.

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29

Elad, M. Super-resolution reconstruction of continuous image sequence : The non-causal approach. Haifa : Department of Electrical Engineering, Technion-Israel Institute of Technology, 1996.

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30

N, Glymour Clark, dir. Discovering causal structure : Artificial intelligence, philosophy of science, and statistical modeling. Orlando : Academic Press, 1987.

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31

Heckman, James J. Causal parameters and policy analysis in economics : A twentieth century retrospective. Cambridge, MA : National Bureau of Economic Research, 1999.

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32

N, Glymour Clark, dir. Discovering causal structure : Artificial intelligence, philosophy of science, and statistical modeling. Orlando : Academic Press, 1987.

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33

Halbert, White, Engle R. F et Granger, C. W. J. 1934-, dir. Cointegration, causality, and forecasting : A festschrift in honour of Clive W.J. Granger. Oxford : Oxford University Press, 1999.

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34

Fell, John P. C. In search of a causal relationship between industrial output and employment in Ireland. Dublin : Central Bank of Ireland, 1989.

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35

Introduction to causal analysis : Exploring survey data by crosstabulation. 2^e éd. Oslo : Norwegian University Press, 1988.

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36

John, Morton. Understanding developmental disorders : A causal modelling approach. Malden, MA : Blackwell Pub., 2004.

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37

Auernheimer, Leonardo. Causality and association between money, prices and government debt. Capital Federal [i.e. Buenos Aires] : C.E.M.A., 1996.

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38

Dufour, Jean-Marie. Short-run and long-run causality between monetary policy variables and stock prices. Ottawa : Bank of Canada, 2006.

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39

Dufour, Jean-Marie. Short-run and long-run causality between monetary policy variables and stock prices. [Ottawa] : Bank of Canada, 2006.

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40

Dufour, Jean-Marie. Short-run and long-run causality between monetary policy variables and stock prices. [Ottawa, Ont.] : Bank of Canada, 2006.

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41

Aulin, Arvid. Causal and stochastic elements in business cycles : An essential extension of macroeconomics leading to improved predictions of data. New York : Springer-Verlag, 1996.

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42

Heimer, Carol Anne. Remodeling the garbage can : Implications of the causal origins of items in decision streams. Chicago, Ill : American Bar Foundation, 1998.

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43

Dehejia, Rajeev H. Causal effects in non-experimental studies : Re-evaluating the evaluation of training programs. Cambridge, MA : National Bureau of Economic Research, 1998.

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44

Solstad, Torgrim, et Oliver Bott. Causality and Causal Reasoning in Natural Language. Sous la direction de Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.32.

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This chapter provides a combined overview of theoretical and psycholinguistic approaches to causality in language. The chapter’s main phenomenological focus is on causal relations as expressed intra-clausally by verbs (e.g., break, open) and between sentences by discourse markers (e.g., because, therefore). Special attention is given to implicit causality verbs that are argued to trigger expectations of explanations to occur in subsequent discourse. The chapter also discusses linguistic expressions that do not encode causation as such, but that seem to be dependent on a causal model for their adequate evaluation, such as counterfactual conditionals. The discussion of the phenomena is complemented by an overview of important aspects of their cognitive processing as revealed by psycholinguistic experimentation.

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45

Hitchcock, Christopher. Causal Modelling. Sous la direction de Helen Beebee, Christopher Hitchcock et Peter Menzies. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199279739.003.0015.

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‘Causal modelling’ is a general term that applies to a wide variety of formal methods for representing, and facilitating inferences about, causal relationships. The end of the twentieth century saw an explosion of work on causal modelling, with contributions from such fields as statistics, computer science, and philosophy; as well as from more subject-specific disciplines such as econometrics and epidemiology. This article focuses on two programmes that have attracted considerable philosophical attention, one due to the computer scientist Judea Pearl and his collaborators, and the other to the philosophers Peter Spirtes, Clark Glymour, and Richard Scheines. It offers a much simplified presentation of causal models that emphasizes various points of philosophical interest.

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46

Rehder, Bob. Concepts as Causal Models. Sous la direction de Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.21.

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This chapter evaluates the case for treating concepts as causal models, the view that people conceive of a categories as consisting of not only features but also the causal relations that link those features. In particular, it reviews the role of causal models in category-based induction. Category-based induction consists of drawing inferences about either objects or categories; in the latter case one generalizes a feature to a category (and thus its members). How causal knowledge influences how categories are formed in the first place—causal-based category discovery—is also examined. Whereas the causal model approach provides a generally compelling account of a large variety of inductive inferences, certain key discrepancies between the theory and empirical findings are highlighted. The chapter concludes with a discussion of the new sorts of representations, tasks, and tests that should be applied to the causal model approach to concepts.

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47

Choe, Minja Kim, et Robert D. Retherford. Statistical Models for Causal Analysis. Wiley & Sons, Incorporated, John, 2011.

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48

Choe, Minja Kim, et Robert D. Retherford. Statistical Models for Causal Analysis. Wiley & Sons, Incorporated, John, 2011.

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49

Coseru, Christian. Consciousness and Causal Emergence. Sous la direction de Jonardon Ganeri. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199314621.013.24.

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In challenging the physicalist conception of consciousness advanced by Cārvāka materialists such as Bṛhaspati, the Buddhist philosopher Śāntarakṣita addresses a series of key issues about the nature of causality and the basis of cognition. This chapter considers whether causal accounts of generation for material bodies are adequate in explaining how conscious awareness comes to have the structural features and phenomenal properties that it does. Arguments against reductive physicalism, it is claimed, can benefit from an understanding of the structure of phenomenal consciousness that does not eschew causal-explanatory reasoning. Against causal models that rely on the concept of potentiality, the Buddhist principle of “dependent arising” underscores a dynamic conception of efficient causality, which allows for elements defined primarily in terms of their capacity for sentience and agency to be causally efficacious.

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Holyoak, Keith J., et Hee Seung Lee. Inferring Causal Relations by Analogy. Sous la direction de Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.25.

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When two situations share a common pattern of relationships among their constituent elements, people often draw an analogy between a familiar source analog and a novel target analog. This chapter reviews major subprocesses of analogical reasoning and discusses how analogical inference is guided by causal relations. Psychological evidence suggests that analogical inference often involves constructing and then running a causal model. It also provides some examples of analogies and models that have been used as tools in science education to foster understanding of critical causal relations. A Bayesian theory of causal inference by analogy illuminates how causal knowledge, represented as causal models, can be integrated with analogical reasoning to yield inductive inferences.

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