Inferring extended summary causal graphs from observational time series
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Abstract (arXiv)
This study addresses the problem of learning an extended summary causal graph on time series. The algorithms we propose fit within the well-known constraint-based framework for causal discovery and make use of information-theoretic measures to determine (in)dependencies between time series. We first introduce generalizations of the causation entropy measure to any lagged or instantaneous relations, prior to using this measure to construct extended summary causal graphs by adapting two well-known algorithms, namely PC and FCI. The behavior of our methods is illustrated through several experiments run on simulated and real datasets.
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References · 7
- 2017Detecting causal associations in large nonlinear time series datasetscited by 240
- 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 198
- 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 136
- 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 82
- 2013Learning Equivalence Classes of Bayesian Networks Structurescited by 75
- 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
- 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 46
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