Learning Causal Structures Using Regression Invariance

@inproceedings{Ghassami2017LearningCS,
  title={Learning Causal Structures Using Regression Invariance},
  author={AmirEmad Ghassami and Saber Salehkaleybar and Negar Kiyavash and Kun Zhang},
  booktitle={NIPS},
  year={2017}
}
We study causal inference 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. We define a notion of completeness for a causal inference algorithm in this setting and prove the existence of such algorithm… CONTINUE READING
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