DeepAI AI Chat
Log In Sign Up

Explanation Trees for Causal Bayesian Networks

by   Ulf Nielsen, et al.

Bayesian networks can be used to extract explanations about the observed state of a subset of variables. In this paper, we explicate the desiderata of an explanation and confront them with the concept of explanation proposed by existing methods. The necessity of taking into account causal approaches when a causal graph is available is discussed. We then introduce causal explanation trees, based on the construction of explanation trees using the measure of causal information ow (Ay and Polani, 2006). This approach is compared to several other methods on known networks.


page 1

page 2

page 3

page 4


Evaluating computational models of explanation using human judgments

We evaluate four computational models of explanation in Bayesian network...

Finding dissimilar explanations in Bayesian networks: Complexity results

Finding the most probable explanation for observed variables in a Bayesi...

What is understandable in Bayesian network explanations?

Explaining predictions from Bayesian networks, for example to physicians...

Towards Causal Explanation Detection with Pyramid Salient-Aware Network

Causal explanation analysis (CEA) can assist us to understand the reason...

A Causal Bayesian Networks Viewpoint on Fairness

We offer a graphical interpretation of unfairness in a dataset as the pr...

Explanation from Specification

Explainable components in XAI algorithms often come from a familiar set ...

Probabilistic Structural Controllability in Causal Bayesian Networks

Humans routinely confront the following key question which could be view...