Explaining data using causal Bayesian networks

Jaime Sevilla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce Causal Bayesian Networks as a formalism for representing and explaining probabilistic causal relations, review the state of the art on learning Causal Bayesian Net-works and suggest and illustrate a research avenue for studying pairwise identification of causal relations inspired by graphical causality criteria
Original languageEnglish
Title of host publication2nd Workshop on Interactive Natural Language Technologyfor Explainable Artificial Intelligence
Subtitle of host publicationProceedings of NL4XAI
Pages34-38
Number of pages5
Publication statusPublished - 18 Dec 2020
Event2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence -
Duration: 18 Dec 202018 Dec 2020
https://sites.google.com/view/nl4xai2020/program

Workshop

Workshop2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence
Period18/12/2018/12/20
Internet address

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