Detecting High-Dimensional Causal Networks Using Randomly Conditioned Granger Causality (CSIAM Transactions on Applied Mathematics, 2021)
Reconstructing faithfully causal networks from observed time series data isfundamental to revealing the intrinsic nature of complex systems. With the increase ofthe network scale, indirect causal relations will arise due to causation transitivity butexisting methods suffer from dimension curse in eliminating such indirect influences.In this paper, we propose a novel technique to overcome the difficulties by integrating the idea of randomly distributed embedding into conditional Granger causality.Validated by both benchmark and synthetic data sets, our method demonstrates potential applicability in reconstructing high-dimensional causal networks based only ona short-term time series.