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A Glance at Causality Theories for Artificial Intelligence

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A Guided Tour of Artificial Intelligence Research

Abstract

Causality plays a key role in the understanding of the world by humans. As such, it has been considered by artificial intelligence researchers from different perspectives ranging from the use of causal links in diagnosis or in reasoning about action to the ascription of causality relations and the assessment of responsibility. In the last two decades, some formal models of causality, such as those proposed by Pearl and Halpern, have been much influential beyond the field of artificial intelligence because they account for the distinction between actual causality and spurious correlations. Yet other aspects of causality modeling are worth of interest, such as the role played by the notion of abnormality, since what we need to explain are often deviations from the normal course of things. The chapter provides a brief but extensive overview of the artificial intelligence literature dealing with causality, albeit without the ambition of giving a complete account of works by philosophers and psychologists that have influenced it.

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Notes

  1. 1.

    In French, “le jambon fait boire; le boire désaltère: par quoi le jambon désaltère”, Michel de Montaigne, Les Essais, Chap. 15, 1580.

  2. 2.

    This work has its roots in works on causal ordering, emphasizing the directed nature of causation, in econometrics, with the pioneering works of Wright (1921), Haavelmo (1943) (see Pearl 2015), continued in early works by Simon (1952, 1953, 1954), Simon and Rescher (1966). Later on, a debate took place about causal ordering and its use for qualitative reasoning in diagnosis (Iwasaki and Simon 1986a, b; de Kleer and Brown 1986).

  3. 3.

    The interest of linear structural equation models for analyzing non-trivial causal phenomena has been emphasized in the recent years by Pearl and his co-authors, see, e.g., Pearl (2013), Chen et al. (2014).

  4. 4.

    This proposal is based on the idea that counterfactuality involves the computation of two kinds of evolutions of the world, namely extrapolation and update. If we want to know whether an action is a counterfactual cause of an event, given a reported sequence of events, we need to (i) compute the most normal evolutions of the world (called trajectories) that correspond to the sequence. This computation is called extrapolation, it is a process of completing initial beliefs sets stemming from observations by assuming minimal abnormalities in the evolution of the world with respect to generic knowledge; (ii) compute what would have happened if some event had not been true. This is done by updating using a distance between trajectories that takes into account the date of the change, and normality.

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Acknowledgements

This paper has partly benefited of the collective work, performed a decade ago, in the 3-year ANR research project MICRAC (2005-2008) dedicated to the study of causality modeling, whose participants included S. Benferhat, J.-F. Bonnefon, Ph. Chassy, R. Da Silva Neves, D. Dubois, F. Dupin de Saint-Cyr, D. Hilton, D. Kayser, F. Levy, F. Nouioua, S. Nouioua-Boutouhami and H. Prade.

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    Didier Dubois & Henri Prade

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Correspondence to Didier Dubois .

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  1. CRIL-CNRS, Université d'Artois and Institut Universitaire de France, Lens, France

    Pierre Marquis

  2. Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France

    Odile Papini

  3. IRIT, CNRS and Université Paul Sabatier, Toulouse, France

    Henri Prade

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Dubois, D., Prade, H. (2020). A Glance at Causality Theories for Artificial Intelligence. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06164-7_9

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