Abstract
I introduce Causal Bayesian Networks as a formalism for representing and explaining probabilistic causal relations, review the state of the art on learning Causal Bayesian Networks and suggest and illustrate a research avenue for studying pairwise identification of causal relations inspired by graphical causality criteria.- Anthology ID:
- 2020.nl4xai-1.8
- Volume:
- 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence
- Month:
- November
- Year:
- 2020
- Address:
- Dublin, Ireland
- Editors:
- Jose M. Alonso, Alejandro Catala
- Venue:
- NL4XAI
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–38
- Language:
- URL:
- https://aclanthology.org/2020.nl4xai-1.8
- DOI:
- Cite (ACL):
- Jaime Sevilla. 2020. Explaining data using causal Bayesian networks. In 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, pages 34–38, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Explaining data using causal Bayesian networks (Sevilla, NL4XAI 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.nl4xai-1.8.pdf