Combining Probabilistic, Causal, and Normative Reasoning in CP-logic
  In recent years the search for a proper formal definition of actual causation
-- i.e., the relation of cause-effect as it is instantiated in specific
observations, rather than general causal relations -- has taken on impressive
proportions. In part this is due to the insight that this concept plays a
fundamental role in many different fields, such as legal theory, engineering,
medicine, ethics, etc. Because of this diversity in applications, some
researchers have shifted focus from a single idealized definition towards a
more pragmatic, context-based account. For instance, recent work by Halpern and
Hitchcock draws on empirical research regarding people's causal judgments, to
suggest a graded and context-sensitive notion of causation. Although we
sympathize with many of their observations, their restriction to a merely
qualitative ordering runs into trouble for more complex examples. Therefore we
aim to improve on their approach, by using the formal language of CP-logic
(Causal Probabilistic logic), and the framework for defining actual causation
that was developed by the current authors using it. First we rephrase their
ideas into our quantitative, probabilistic setting, after which we modify it to
accommodate a greater class of examples. Further, we introduce a formal
distinction between statistical and normative considerations.
