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Poster
Learning Causal Structures Using Regression Invariance
AmirEmad Ghassami · Saber Salehkaleybar · Negar Kiyavash · Kun Zhang

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #186
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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.

Keywords: Information Theory · Model Selection and Structure Learning · Graphical Models · Regression · Causal Inference
[ Paper] 

Author Information

AmirEmad Ghassami (University of Illinois at Urbana–Champaign)
Saber Salehkaleybar (University of Illinois at Urbana-Champaign)
Negar Kiyavash (Georgia Tech)
Kun Zhang (CMU)

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