ML Anthology

Inferring Causal Directions in Errors-in-Variables Models

Yulai Zhang, Guiming Luo
AAAI 2014 pp. 3152-3153
doi:10.1609/AAAI.V28I1.9079 /aaai/2014/zhang2014aaai-inferring/

Abstract

Inferring the causal direction between two variables is a nontrivial problem in the subject of causal discovery from observed data. A method for errors-in-variables models where both the cause variable and the effect variable are observed with measurement errors is presented in this paper.

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Text

Zhang and Luo. "Inferring Causal Directions in Errors-in-Variables Models." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9079

Markdown

[Zhang and Luo. "Inferring Causal Directions in Errors-in-Variables Models." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/zhang2014aaai-inferring/) doi:10.1609/AAAI.V28I1.9079

BibTeX

@inproceedings{zhang2014aaai-inferring,
  title     = {{Inferring Causal Directions in Errors-in-Variables Models}},
  author    = {Zhang, Yulai and Luo, Guiming},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {3152-3153},
  doi       = {10.1609/AAAI.V28I1.9079},
  url       = {https://mlanthology.org/aaai/2014/zhang2014aaai-inferring/}
}