Causal Discovery from Discrete Data using Hidden Compact Representation

NeurIPS 2018 Ruichu CaiJie QiaoKun ZhangZhenjie ZhangZhifeng Hao

Causal discovery from a set of observations is one of the fundamental problems across several disciplines. For continuous variables, recently a number of causal discovery methods have demonstrated their effectiveness in distinguishing the cause from effect by exploring certain properties of the conditional distribution, but causal discovery on categorical data still remains to be a challenging problem, because it is generally not easy to find a compact description of the causal mechanism for the true causal direction... (read more)

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