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In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS).
Investigating causal relations by econometric models and cross-spectral methods
C. W. J. Granger · 1969
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Causation, Prediction, and Search
P. Spirtes, C. N. Glymour, and R. Scheines · 2001
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N. Wiener · 2002
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Kernel-Granger causality and the analysis of dynamical networks
D. Marinazzo, M. Pellicoro, and S. Stramaglia · 2008
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Causality
J. Pearl · 2009
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Nonlinear connectivity by Granger causality
D. Marinazzo, W. Liao, H. Chen, and S. Stramaglia · 2011
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Expanding the transfer entropy to identify information circuits in complex systems
S. Stramaglia, G.-R. Wu, M. Pellicoro, and D. Marinazzo · 2012
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Learning causal structure from undersampled time series
D. Danks and S. Plis · 2013
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Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
IPCC · 2013
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From Ordinary Differential Equations to Structural Causal Models: the deterministic case
J. M. Mooij, D. Janzing, and B. Schölkopf · 2013
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Identifiability of Gaussian structural equation models with equal error variances
J. Peters and P. Bühlmann · 2014
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Synergy and redundancy in the Granger causal analysis of dynamical networks
S. Stramaglia, J. M. Cortes, and D. Marinazzo · 2014
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Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Causal consistency of structural equation models
P. K. Rubenstein, S. Weichwald, S. Bongers, J. M. Mooij, D. Janzing, M. Grosse-Wentrup, and B. Schölkopf · 2017
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From random differential equations to structural causal models: The stochastic case
S. Bongers and J. M. Mooij · 2018
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Structural causal models for macro-variables in time-series
D. Janzing, P. K. Rubenstein, and B. Schölkopf · 2018
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From deterministic ODEs to dynamic structural causal models
P. K. Rubenstein, S. Bongers, J. M. Mooij, and B. Schölkopf · 2018
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Markov equivalence of marginalized local independence graphs
S. W. Mogensen and N. R. Hansen · 2020
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A. Hyttinen, S. Plis, M. Järvisalo, F. Eberhardt, and D. Danks · 2016
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Elements of Causal Inference
J. Peters, D. Janzing, and B. Schölkopf · 2017
Cited alongside, same era.
Inferring causation from time series in Earth system sciences
J. Runge, S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, M. Kretschmer, M. D. Mahecha, E. H. Muñoz-Marí, J. andand van Nes, J. Peters, R. Quax, M. Reichstein, M. Scheffer, B. Schölkopf, P. Spirtes, G. Sugihara, J. Sun, K. Zhang, and J. Zscheischler
Cited in the paper.
Detecting and quantifying causal associations in large nonlinear time series datasets
J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, and D. Sejdinovic
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J. Peters, S. Bauer, and N. Pfister · 2020
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The causality for climate competition
J. Runge, X.-A. Tibau, M. Bruhns, J. Muñoz-Marí, and G. Camps-Valls · 2020
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