A new approach for defining causality and such related notions as degree of responsibility,
degrees of blame, and causal explanation. Causality plays a central role in the way people …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Written by one of the preeminent researchers in the field, this book provides a
comprehensive exposition of modern analysis of causation. It shows how causality has …

As artificial intelligence is being increasingly used for high-stakes applications, it is
becoming more and more important that the models used be interpretable. Bayesian …

We propose new definitions of (causal) explanation, using structural equations to model
counterfactuals. The definition is based on the notion of actual cause, as defined and …

When evaluating causal explanations, simpler explanations are widely regarded as better
explanations. However, little is known about how people assess simplicity in causal …

This article is an edited transcript of a lecture given at IJCAI-99, Stockholm, Sweden, on 4
August 1999. The article summarizes concepts, principles, and tools that were found useful …

Actual causation is concerned with the question:“What caused what?” Consider a transition
between two states within a system of interacting elements, such as an artificial neural …

In this paper, we review the role of probabilistic graphical models in artificial intelligence. We
start by giving an account of the early years when there was important controversy about the …

Causal effect identification considers whether an interventional probability distribution can
be uniquely determined from a passively observed distribution in a given causal structure. If …