130 citations · 671 across the 42 of their papers we have counts for
5 papers · 1 filter
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur +1
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence. These values are designed to attribute the difference between a m…
Learning Sparse Causal Models is not NP-hard
Tom Claassen, Joris Mooij, Tom Heskes
This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obta…
Expectation Propogation for approximate inference in dynamic Bayesian networks
Tom Heskes, Onno Zoeter
We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl s exact belief propagation.Expectation propagation IS a g…
A Bayesian Approach to Constraint Based Causal Inference
Tom Claassen, Tom Heskes
We target the problem of accuracy and robustness in causal inference from finite data sets. Some state-of-the-art algorithms produce clear output complete with solid theoretical gu…
A Logical Characterization of Constraint-Based Causal Discovery
Tom Claassen, Tom Heskes
We present a novel approach to constraint-based causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about ca…