Actual or token causation aims to identify the causes of an event in a specific scenario. A broad range of inquiries might motivate the search for actual causes, for instance: Which factors were involved in bringing about the effect? How could we, in similar instances, prevent the effect from happening in the future? Who should we hold responsible for bringing about the effect? etc. In the past decade there have been a number of attempts to formalize this concept using causal models, perhaps most notably is the approach of Halpern and Pearl (Halpern and Pearl: 2005). However, in light of the diversity in underlying motivations, attempts to construct a general account of actual causation risk to be in vain. Recently this criticism was addressed to some extent by combining causal models with so-called defaults (Halpern and Hitchcock: 2010), albeit still without specifying the underlying inquiries in any more detail. In this talk I want to develop this line of thought further and explore some related issues which haven't been thoroughly discussed yet: often the underlying inquiry requires a more quantitative answer, for instance: What is the most efficient way to prevent the effect in future? To what extent did the cause contribute in bringing about the effect? etc. This certainly becomes apparent when an effect is caused by multiple factors. In light of this last issue I will relate present work with some older work of I.J. Good (I.J. Good 1961/62;1994), who seems to have anticipated much of the issues currently under attention.
Good, I.J. (1961/62): A Causal Calculus. British Journal for the Philosophy of Science.
Good, I.J. (1994): Causal tendency, necessitivity and sufficientivity: an updated review. In Patrick Suppes, Scientific Philosopher. Paul Humphreys, ed.; Kluwer Dordrecht, Vol. 1, 293-315.
Halpern, J.Y. and J. Pearl (2005): Causes and explanations: A structural-model approach. Part I: Causes. British Journal for Philosophy of Science 56, 843-887.
Halpern, J.Y. (2008): Defaults and normality in causal structures. In Principles of Knowledge Representation and Reasoning: Proc. Eleventh International Conference, 198-208.
Halpern, J.Y. and C. Hitchcock (2010): Actual Causation and the Art of Modeling. In Heuristics, Probability and Causality. A Tribute to Judea Pearl. R. Dechter, H. Geffner, J.Y. Halpern, ed.; College Publications, 383-406
Pearl, J. (2000): Causality: Models, Reasoning, and Inference. Cambridge University Press.
Keywords: causal inference; Bayesian networks
Biography: Tim De Craecker, Phd student, University Gent