@Article{CSIAM-AM-2-680, author = { and Huanfei Ma and and 21495 and and Huanfei Ma and and Siyang Leng and and 21496 and and Siyang Leng and Luonan and Chen and and 21497 and and Luonan Chen}, title = {Detecting High-Dimensional Causal Networks Using Randomly Conditioned Granger Causality}, journal = {CSIAM Transactions on Applied Mathematics}, year = {2021}, volume = {2}, number = {4}, pages = {680--696}, abstract = {

Reconstructing faithfully causal networks from observed time series data is fundamental to revealing the intrinsic nature of complex systems. With the increase of the network scale, indirect causal relations will arise due to causation transitivity but existing 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 on a short-term time series.

}, issn = {2708-0579}, doi = {https://doi.org/10.4208/csiam-am.2020-0184}, url = {http://global-sci.org/intro/article_detail/csiam-am/19988.html} }