A Subsampling-Based Method for Causal Discovery on Discrete Data
- URL: http://arxiv.org/abs/2108.13984v2
- Date: Wed, 1 Sep 2021 15:30:59 GMT
- Title: A Subsampling-Based Method for Causal Discovery on Discrete Data
- Authors: Austin Goddard and Yu Xiang
- Abstract summary: In this work, we propose a subsampling-based method to test the independence between the generating schemes of the cause and that of the mechanism.
Our methodology works for both discrete and categorical data and does not imply any functional model on the data, making it a more flexible approach.
- Score: 18.35147325731821
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Inferring causal directions on discrete and categorical data is an important
yet challenging problem. Even though the additive noise models (ANMs) approach
can be adapted to the discrete data, the functional structure assumptions make
it not applicable on categorical data. Inspired by the principle that the cause
and mechanism are independent, various methods have been developed, leveraging
independence tests such as the distance correlation measure. In this work, we
take an alternative perspective and propose a subsampling-based method to test
the independence between the generating schemes of the cause and that of the
mechanism. Our methodology works for both discrete and categorical data and
does not imply any functional model on the data, making it a more flexible
approach. To demonstrate the efficacy of our methodology, we compare it with
existing baselines over various synthetic data and real data experiments.
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