Reconstructing regime-dependent causal relationships from observational time series
- Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past few decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this difficult task. In this paper, we focus on an important challenge that is at the core of time series causal discovery: regime-dependent causal relations. Often dynamical systems feature transitions depending on some, often persistent, unobserved background regime, and different regimes may exhibit different causal relations. Here, we assume a persistent and discrete regime variable leading to a finite number of regimes within which we may assume stationary causal relations. To detect regime-dependent causal relations, we combine the conditional independence-based PCMCI method [based on a condition-selection step (PC) followed by the momentary conditional independence (MCI) test] with a regimeInferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past few decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this difficult task. In this paper, we focus on an important challenge that is at the core of time series causal discovery: regime-dependent causal relations. Often dynamical systems feature transitions depending on some, often persistent, unobserved background regime, and different regimes may exhibit different causal relations. Here, we assume a persistent and discrete regime variable leading to a finite number of regimes within which we may assume stationary causal relations. To detect regime-dependent causal relations, we combine the conditional independence-based PCMCI method [based on a condition-selection step (PC) followed by the momentary conditional independence (MCI) test] with a regime learning optimization approach. PCMCI allows for causal discovery from high-dimensional and highly correlated time series. Our method, Regime-PCMCI, is evaluated on a number of numerical experiments demonstrating that it can distinguish regimes with different causal directions, time lags, and sign of causal links, as well as changes in the variables' autocorrelation. Furthermore, Regime-PCMCI is employed to observations of El Nino Southern Oscillation and Indian rainfall, demonstrating skill also in real-world datasets.…
Author details: | Elena SaggioroORCiD, Jana de WiljesORCiDGND, Marlene KretschmerORCiD, Jakob RungeORCiD |
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DOI: | https://doi.org/10.1063/5.0020538 |
ISSN: | 1054-1500 |
ISSN: | 1089-7682 |
ISSN: | 1527-2443 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/33261320 |
Title of parent work (English): | Chaos : an interdisciplinary journal of nonlinear science |
Publisher: | American Institute of Physics |
Place of publishing: | Melville |
Publication type: | Article |
Language: | English |
Date of first publication: | 2020/11/06 |
Publication year: | 2020 |
Release date: | 2023/01/25 |
Volume: | 30 |
Issue: | 11 |
Article number: | 113115 |
Number of pages: | 22 |
Funding institution: | Centre for Doctoral Training in Mathematics of Planet Earth, UK EPSRCUK; Research & Innovation (UKRI)Engineering & Physical Sciences Research; Council (EPSRC) [EP/L016613/1]; ERC Advanced Grant ACRCC [339390]; Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG); [SFB1294/1-318763901]; Simons CRM Scholar-in-Residence Program; European; Union's Horizon 2020 research and innovation programme under the Marie; Sklodowska-Curie grant [841902] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
DDC classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik | |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |