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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.
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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2020 Workshop: Causal Discovery and Causality-Inspired Machine Learning »
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2018 Poster: Multi-domain Causal Structure Learning in Linear Systems »
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2018 Poster: Predictive Approximate Bayesian Computation via Saddle Points »
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2017 Poster: Online Learning for Multivariate Hawkes Processes »
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