Entropy-based Discovery of Summary Causal Graphs in Time Series

TL;DR

This paper introduces a novel entropy-based measure for discovering summary causal graphs in time series data with varying sampling rates, enhancing causal inference methods.

Contribution

It proposes a new causal temporal mutual information measure and integrates it into PC-like and FCI-like algorithms for effective causal graph discovery.

Findings

Algorithms are effective on multiple datasets.

Methods demonstrate high efficiency.

Causal inference is improved in multi-rate time series.

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.