Imperfect Causality: Combining Experimentation and Theory

  • Alejandro Sobrino
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 271)

Abstract

This paper is a journey around causality, imperfect causality, causal models and experiments for testing hypothesis about what causality is, with special attention to imperfect causality. Causal relations are compared with logic relations and analogies and differences are highlighted. Classical properties of causality are described and one characteristic more is added: causes, effects and the cause-effect links usually are qualified by different degrees of strength. Causal sentences automatically recovered from texts show this. In daily life, imperfect causality has an extensive role in causal decision-making. Bayes Nets offer an appropriate model to characterize causality in terms of conditional probabilities, explaining not only how choices are made but also how to learn new causal squemes based on the previously specified. Psychological experiments seem to support this view. But Bayes Nets have an Achilles hell: if the names labeling nodes are vague in meaning, the probability cannot be specified in an exact way. Fuzzy logic offers models to deals with vagueness in language. Kosko fuzzy cognitive maps provide the classical way to address fuzzy causalility. Other less relevant models to manage imperfect causality are proposed, but fuzzy people still lacks of a comprehensive batterie of examples to test those models about how fuzzy causality works. We provide a program that retrieves causal and conditional causal sentences from texts and authomatically depicts a graph representing causal concepts as well as the links between them, including fuzzy quantifiers and semantic hedges modifying nodes and links. Get these mechanisms can provide a benchmark to test hyphotesis about what is fuzzy causality, contributing to improve the current models.

Keywords

Bayesian Network Fuzzy Relation Secondhand Smoke Causal Principle Causal Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aish-Van Vaerenbergh, A.M.: Explanatory models in suicide research: explaining relationships. In: Frank, R. (ed.) The Explanatory Power of Models, pp. 51–66. Kluwer, DordrechtGoogle Scholar
  2. 2.
    Mellor, D. H.: The facts of causation, Routledge (1995)Google Scholar
  3. 3.
    Hage, J., Meeker, B.F.: Social Causality. Allen &Unwin, Inc. (1988)Google Scholar
  4. 4.
    Kohut, B., MacDuffie, J.P., Ragin, C.: Prototypes and Strategy: Assigning Causal Credit Using Fuzzy Sets. European Management Review 1(2), 114–131 (2004)CrossRefGoogle Scholar
  5. 5.
    Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2007)CrossRefMATHGoogle Scholar
  6. 6.
    Pearl, J.: Causality Models, Reasoning and Inference, 2nd edn. Cambridge University Press (2009)Google Scholar
  7. 7.
    Puente, C.: Extraction and analysis of conditionaland causal sentences for Information Retrieval. Thesis, ICAI Madrid (2010)Google Scholar
  8. 8.
    Popper, K.: Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge Classics (1963)Google Scholar
  9. 9.
    Hempel, C.: Aspects of scientific explanation. In: Aspects of scientific explanation and other essays in the Philosophy of Science, pp. 331–496. The Free Press (1965)Google Scholar
  10. 10.
    Davidson, D.: Essays on Actions and Events. Clarendom Press, Oxford (1980)Google Scholar
  11. 11.
    Bunge, M.: Causality and modern science. Dover (1979)Google Scholar
  12. 12.
    Weinert, F.: The Scientist as Philosopher. Philosophical Consequences of Great Scientific Discoveries. Springer, Heidelberg (2005)Google Scholar
  13. 13.
    Williamson, J.: Bayesian Nets and Causality. Oxford University Press (2005)Google Scholar
  14. 14.
    Nicholson, A.E., Korb, K.B.: Bayesian Artificial Intelligence. Chapman & Hall/CRC (2004)Google Scholar
  15. 15.
    Tenembaum, J., et al.: Intuitive theories as grammars for causal inference. In: Gopnik, A., Schultz, L. (eds.) Causal Learning. Psychology, Philosophy and Computation. Oxford University Press (2007)Google Scholar
  16. 16.
    Gopnik, A., et al.: A theory of causal learning in children. Causal maps and Bayes Net. Psychological Review 111(1), 3–32 (2004)CrossRefGoogle Scholar
  17. 17.
    Gopnik, A., Sobel, D.M.: Detecting blickets: How young children use information about causal properties in categorization and induction. Child Development 71, 1205–1222Google Scholar
  18. 18.
    Cooper, G.F.: An overview of the Representation and Discovery of Causal Relationships using Bayesian Networks. In: Glymour, C., Cooper, G.F. (eds.) Computation, Causation and Discovery. The MIT Press (1999)Google Scholar
  19. 19.
    Bellman, R., Zadeh, L.A.: Local and fuzzy logics. In: Dunn, J.M., Epstein, G.E. (eds.) Modern Uses of Multiple-Valued Logics, pp. 103–165. Reidel (1977)Google Scholar
  20. 20.
    Kosko, B.: Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24, 65–75 (1986)CrossRefMATHGoogle Scholar
  21. 21.
    Kim, H.S., Lee, K.C.: Fuzzy Implications of fuzzy cognitive maps with emphasis on fuzzy causal relationship and fuzzy partially causal relationship. Fuzzy Sets and Systems, 303–313 (1998)Google Scholar
  22. 22.
    Puente, C., et al.: Extraction, Analysis and Representation of Imperfect Conditional and Causal Sentences by means of a Semi-Automatic Process. In: WCCI 2010 IEEE World Congress on Computational Intelligence, Proceedings of CCIB, FUZZ- IEEE, July 18-23, pp. 1423–1430, Spain (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alejandro Sobrino
    • 1
  1. 1.Faculty of PhilosophyUniversity of Santiago de CompostelaSantiago de CompostelaSpain

Personalised recommendations