Learning Causal Structures Using Regression Invariance
Abstract
We study causal discovery in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary. We introduce the idea of using the invariance of the functional relations of the variables to their causes across a set of environments for structure learning. We define a notion of completeness for a causal inference algorithm in this setting and prove the existence of such algorithm by proposing the baseline algorithm. Additionally, we present an alternate algorithm that has significantly improved computational and sample complexity compared to the baseline algorithm. Experiment results show that the proposed algorithm outperforms the other existing algorithms.
Cite
Text
Ghassami et al. "Learning Causal Structures Using Regression Invariance." Neural Information Processing Systems, 2017.Markdown
[Ghassami et al. "Learning Causal Structures Using Regression Invariance." Neural Information Processing Systems, 2017.](https://mlanthology.org/neurips/2017/ghassami2017neurips-learning/)BibTeX
@inproceedings{ghassami2017neurips-learning,
title = {{Learning Causal Structures Using Regression Invariance}},
author = {Ghassami, AmirEmad and Salehkaleybar, Saber and Kiyavash, Negar and Zhang, Kun},
booktitle = {Neural Information Processing Systems},
year = {2017},
pages = {3011-3021},
url = {https://mlanthology.org/neurips/2017/ghassami2017neurips-learning/}
}