TY - JOUR
T1 - Detecting High-Dimensional Causal Networks Using Randomly Conditioned Granger Causality
AU - Huanfei Ma , 
AU - Siyang Leng , 
AU - Chen , Luonan
JO - CSIAM Transactions on Applied Mathematics
VL - 4
SP - 680
EP - 696
PY - 2021
DA - 2021/11
SN - 2
DO - http://doi.org/10.4208/csiam-am.2020-0184
UR - https://global-sci.org/intro/article_detail/csiam-am/19988.html
KW - Network reconstruction, Granger causality, conditional causality, randomly distributed embedding.
AB - <p style="text-align: justify;">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.</p>