Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical B...
Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
We begin by discussing causal independence models and generalize these models to causal interaction models. Causal interaction models are models that have independent mechanisms w...
The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl’s Causal Model, and so far their re...
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this pap...
Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge...
Marc Maier, Brian Taylor, Huseyin Oktay, David Jen...