Explaining data using causal Bayesian networks

Jaime Sevilla

Explaining data using causal Bayesian networks

Jaime Sevilla

School of Natural & Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

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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 language	English
Title of host publication	2nd Workshop on Interactive Natural Language Technologyfor Explainable Artificial Intelligence
Subtitle of host publication	Proceedings of NL4XAI
Pages	34-38
Number of pages	5
Publication status	Published - 18 Dec 2020
Event	2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence - Duration: 18 Dec 2020 → 18 Dec 2020 https://sites.google.com/view/nl4xai2020/program

Workshop

Workshop	2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence
Period	18/12/20 → 18/12/20
Internet address	https://sites.google.com/view/nl4xai2020/program

Bibliographical note

Acknowledgements:
I thank my supervisors Ehud Reiter and NavaTintarev for thorough discussion and support.I also thank the anonymous reviewers for the NL4XAI for kindly providing constructive feed-back to improve the paper. This research has been supported by the NL4XAI project, which is funded under the Eu-ropean Union’s Horizon 2020 programme, grant agreement 860621

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Sevilla_et_al_NL4XAI_ExplainingDataUsing_VoR
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Sevilla, J 2020, Explaining data using causal Bayesian networks. in 2nd Workshop on Interactive Natural Language Technologyfor Explainable Artificial Intelligence: Proceedings of NL4XAI. pp. 34-38, 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, 18/12/20. <https://nextcloud.citius.usc.es/index.php/s/yRHkFb2Ms6nBjjm#pdfviewer>

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title = "Explaining data using causal Bayesian networks",

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",

author = "Jaime Sevilla",

note = "Acknowledgements: I thank my supervisors Ehud Reiter and NavaTintarev for thorough discussion and support.I also thank the anonymous reviewers for the NL4XAI for kindly providing constructive feed-back to improve the paper. This research has been supported by the NL4XAI project, which is funded under the Eu-ropean Union{\textquoteright}s Horizon 2020 programme, grant agreement 860621; 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence ; Conference date: 18-12-2020 Through 18-12-2020",

year = "2020",

month = dec,

day = "18",

language = "English",

isbn = "978-1-952148-56-9",

pages = "34--38",

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AU - Sevilla, Jaime

N1 - Acknowledgements: I thank my supervisors Ehud Reiter and NavaTintarev for thorough discussion and support.I also thank the anonymous reviewers for the NL4XAI for kindly providing constructive feed-back to improve the paper. This research has been supported by the NL4XAI project, which is funded under the Eu-ropean Union’s Horizon 2020 programme, grant agreement 860621

PY - 2020/12/18

Y1 - 2020/12/18

N2 - 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

AB - 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

UR - https://sites.google.com/view/nl4xai2020/program

M3 - Published conference contribution

SN - 978-1-952148-56-9

SP - 34

EP - 38

BT - 2nd Workshop on Interactive Natural Language Technologyfor Explainable Artificial Intelligence

T2 - 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence

Y2 - 18 December 2020 through 18 December 2020

ER -

Explaining data using causal Bayesian networks

Abstract

Workshop

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