Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge
Abstract
Great efforts have been devoted to causal discovery from observational data, and it is well known that introducing some background knowledge attained from experiments or human expertise can be very helpful. However, it remains unknown that \emph{what causal relations are identifiable given background knowledge in the presence of latent confounders}. In this paper, we solve the problem with sound and complete orientation rules when the background knowledge is given in a \emph{local} form. Furthermore, based on the solution to the problem, this paper proposes a general active learning framework for causal discovery in the presence of latent confounders, with its effectiveness and efficiency validated by experiments.
Cite
Text
Wang et al. "Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge." Neural Information Processing Systems, 2022.Markdown
[Wang et al. "Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/wang2022neurips-sound/)BibTeX
@inproceedings{wang2022neurips-sound,
title = {{Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge}},
author = {Wang, Tian-Zuo and Qin, Tian and Zhou, Zhi-Hua},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/wang2022neurips-sound/}
}