Poster
Learning Causal Structures Using Regression Invariance
AmirEmad Ghassami · Saber Salehkaleybar · Negar Kiyavash · Kun Zhang
Pacific Ballroom #186
Keywords: [ Causal Inference ] [ Regression ] [ Graphical Models ] [ Model Selection and Structure Learning ] [ Information Theory ]
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.
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