Entropy-based Discovery of Summary Causal Graphs in Time Series

• URL: http://arxiv.org/abs/2105.10381v2
• Date: Wed, 1 Nov 2023 19:50:29 GMT
• Title: Entropy-based Discovery of Summary Causal Graphs in Time Series
• Authors: Charles K. Assaad, Emilie Devijver, Eric Gaussier
• Abstract summary: We first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle. We combine these two ingredients in PC-like and FCI-like algorithms to construct the summary causal graph.
• Score: 3.360922672565234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle that can be seen as a special case of the probability raising principle. We finally combine these two ingredients in PC-like and FCI-like algorithms to construct the summary causal graph. There algorithm are evaluated on several datasets, which shows both their efficacy and efficiency.

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