Imperfect Causality: Combining Experimentation and Theory

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


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.


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.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

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

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