Mitigating the effects of measurement noise on Granger causality
Open accessAbstract
Computing Granger causal relations among bivariate experimentally observed time series has received increasing attention over the past few years. Such causal relations, if correctly estimated, can yield significant insights into the dynamical organization of the system being investigated. Since experimental measurements are inevitably contaminated by noise, it is thus important to understand the effects of such noise on Granger causality estimation. The first goal of this paper is to provide an analytical and numerical analysis of this problem. Specifically, we show that, due to noise contamination, (1) spurious causality between two measured variables can arise and (2) true causality can be suppressed. The second goal of the paper is to provide a denoising strategy to mitigate this problem. Specifically, we propose a denoising algorithm based on the combined use of the Kalman filter theory and the expectation-maximization algorithm. Numerical examples are used to demonstrate the effectiveness of the denoising approach.
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Granger causality may not be appropriate for fMRI time-series and performs poorly in comparison to structural (non-dynamic) approaches based upon partial correlations.
“However, there is a growing appreciation that Granger causality may not be appropriate for fMRI time-series (e.g., Nalatore et al 2007) and performs poorly in comparison to structural (non-dynamic) approaches based upon partial correlations (Smith et al., 2010).”
From Network discovery with DCM (2011) · cites this paper as Nalatore et al 2007
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- Authors
- Nalatore, H., Ding, M., & Rangarajan, G.
- Affiliations
- University of Florida (US)
- ORCID
- Ding, Mingzhou
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2007-03-29
- Volume
- 75
- Issue
- 3
- Pages
- 031123-031123
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2154674952
- Cited by
- 98
- References
- 34
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 3 · 2022: 6 · 2021: 4 · 2020: 8 · 2019: 3 · 2018: 7 · 2017: 11 · 2016: 7 · 2015: 7 · 2014: 11 · 2013: 5 · 2012: 3
- FWCI
- 10.80
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review E (publishedVersion)
- PubMed (publishedVersion)
- NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defence Research and Development Organisation
- National Institutes of Health
- National Institute of Mental Health (MH071620, R21MH071620)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Chaos control and synchronization
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- Granger causality, measurement noise, spurious causality, noise reduction, time series denoising, Kalman filter, expectation-maximization algorithm, bivariate time series, causal inference
- MeSH terms
- Computer Simulation, Data Interpretation, Statistical, Epidemiologic Methods, Models, Biological, Sensitivity and Specificity, Statistics as Topic, Models, Statistical, Causality, Epidemiologic Measurements, Artifacts
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