scholarly journals Causal inference from noisy time-series data — Testing the Convergent Cross-Mapping algorithm in the presence of noise and external influence

2017 ◽  
Vol 73 ◽  
pp. 52-62 ◽  
Author(s):  
Dan Mønster ◽  
Riccardo Fusaroli ◽  
Kristian Tylén ◽  
Andreas Roepstorff ◽  
Jacob F. Sherson
Keyword(s):  
Time Series ◽  
Causal Inference ◽  
Time Series Data ◽  
Series Data ◽  
External Influence ◽  
Mapping Algorithm ◽  
Noisy Time Series ◽  
Convergent Cross Mapping

scholarly journals Informing management decisions for ecological networks, using dynamic models calibrated to noisy time‐series data

2020 ◽  
Vol 23 (4) ◽  
pp. 607-619 ◽  
Author(s):  
Matthew P. Adams ◽  
Scott A. Sisson ◽  
Kate J. Helmstedt ◽  
Christopher M. Baker ◽  
Matthew H. Holden ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Time Series Data ◽  
Ecological Networks ◽  
Series Data ◽  
Management Decisions ◽  
Noisy Time Series

scholarly journals Causal inference with multiple time series: principles and problems

2013 ◽  
Vol 371 (1997) ◽  
pp. 20110613 ◽  
Author(s):  
Michael Eichler
Keyword(s):  
Time Series ◽  
Causal Inference ◽  
Granger Causality ◽  
Latent Variables ◽  
Time Series Data ◽  
Series Data ◽  
Multiple Time ◽  
Identification Algorithm ◽  
Multiple Time Series ◽  
Theoretical Justification

I review the use of the concept of Granger causality for causal inference from time-series data. First, I give a theoretical justification by relating the concept to other theoretical causality measures. Second, I outline possible problems with spurious causality and approaches to tackle these problems. Finally, I sketch an identification algorithm that learns causal time-series structures in the presence of latent variables. The description of the algorithm is non-technical and thus accessible to applied scientists who are interested in adopting the method.

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