Entropy-based Discovery of Summary Causal Graphs in Time Series
This addresses a domain-specific problem for researchers analyzing causal relationships in time series data, representing an incremental advance.
The study tackled the problem of learning summary causal graphs from time series with varying sampling rates by proposing a new causal temporal mutual information measure and combining it with PC-like and FCI-like algorithms, showing efficacy and efficiency in evaluations on several datasets.
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.