Web: http://arxiv.org/abs/2206.08448

June 20, 2022, 1:10 a.m. | Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji

cs.LG updates on arXiv.org arxiv.org

Causal discovery is to learn cause-effect relationships among variables given
observational data and is important for many applications. Existing causal
discovery methods assume data sufficiency, which may not be the case in many
real world datasets. As a result, many existing causal discovery methods can
fail under limited data. In this work, we propose Bayesian-augmented
frequentist independence tests to improve the performance of constraint-based
causal discovery methods under insufficient data: 1) We firstly introduce a
Bayesian method to estimate mutual …

arxiv bayesian data discovery lg

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