Takeshi Teshima, Masashi Sugiyama. Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. In Cassio P. de Campos, Marloes H. Maathuis, Erik Quaeghebeur, editors, Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Virtual Event, 27-30 July 2021. Volume 161 of Proceedings of Machine Learning Research, pages 86-96, AUAI Press, 2021. [doi]
@inproceedings{TeshimaS21,
title = {Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation},
author = {Takeshi Teshima and Masashi Sugiyama},
year = {2021},
url = {https://proceedings.mlr.press/v161/teshima21a.html},
researchr = {https://researchr.org/publication/TeshimaS21},
cites = {0},
citedby = {0},
pages = {86-96},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Virtual Event, 27-30 July 2021},
editor = {Cassio P. de Campos and Marloes H. Maathuis and Erik Quaeghebeur},
volume = {161},
series = {Proceedings of Machine Learning Research},
publisher = {AUAI Press},
}