Actual Causation in CP-logic

Given a causal model of some domain and a particular story that has taken place in this domain, the problem of actual causation is deciding which of the possible causes for some effect actually caused it. One of the most influential approaches to this problem has been developed by Halpern and Pearl in the context of structural models. In this paper, I argue that this is actually not the best setting for studying this problem. As an alternative, I offer the probabilistic logic programming language of CP-logic. Unlike structural models, CP-logic incorporates the deviant/default distinction that is generally considered an important aspect of actual causation, and it has an explicitly dynamic semantics, which helps to formalize the stories that serve as input to an actual causation problem

A principled approach to defining actual causation

In this paper we present a new proposal for defining actual causation, i.e., the problem of deciding if one event caused another. We do so within the popular counterfactual tradition initiated by Lewis, which is characterised by attributing a fundamental role to counterfactual dependence. Unlike the currently prominent definitions, our approach proceeds from the ground up: we start from basic principles, and construct a definition of causation that satisfies them. We define the concepts of counterfactual dependence and production, and put forward principles such that dependence is an unnecessary but sufficient condition for causation, whereas production is an insufficient but necessary condition. The resulting definition of causation is a suitable compromise between dependence and production. Every principle is introduced by means of a paradigmatic example of causation. We illustrate some of the benefits of our approach with two examples that have spelled trouble for other accounts. We make all of this formally precise using structural equations, which we extend with a timing over all events

Application of Logic-Based Methods to Machine Component Design

This paper describes an application worked out in collaboration with a company that produces made-to-order machine components. The goal of the project is to develop a system that can support the company\u27s engineers by automating parts of their component design process. We propose a knowledge extraction methodology based on the recent DMN (Decision Model and Notation) standard and compare a rule-based and a constraint-based method for representing the resulting knowledge. We study the advantages and disadvantages of both approaches in the context of the company\u27s real-life application

The Transitivity and Asymmetry of Actual Causation

The counterfactual tradition to defining actual causation has come a long way since Lewis started it off. However there are still important open problems that need to be solved. One of them is the (in)transitivity of causation. Endorsing transitivity was a major source of trouble for the approach taken by Lewis, which is why currently most approaches reject it. But transitivity has never lost its appeal, and there is a large literature devoted to understanding why this is so. Starting from a survey of this work, we will develop a formal analysis of transitivity and the problems it poses for causation. This analysis provides us with a sufficient condition for causation to be transitive, a sufficient condition for dependence to be necessary for causation, and several characterisations of the transitivity of dependence. Finally, we show how this analysis leads naturally to several conditions a definition of causation should satisfy, and use those to suggest a new definition of causation