Volume 2, Issue 4
Detecting High-Dimensional Causal Networks Using Randomly Conditioned Granger Causality

Huanfei Ma, Siyang Leng & Luonan Chen

CSIAM Trans. Appl. Math., 2 (2021), pp. 680-696.

Published online: 2021-11

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  • 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.

  • Keywords

Network reconstruction, Granger causality, conditional causality, randomly distributed embedding.

  • AMS Subject Headings

62D20, 37M10

  • Copyright

COPYRIGHT: © Global Science Press

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@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} }
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 -

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.

Huanfei Ma, Siyang Leng & Luonan Chen. (2021). Detecting High-Dimensional Causal Networks Using Randomly Conditioned Granger Causality. CSIAM Transactions on Applied Mathematics. 2 (4). 680-696. doi:10.4208/csiam-am.2020-0184
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