Entropy-based Discovery of Summary Causal Graphs in Time Series
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Abstract (arXiv)
This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle that can be seen as a special case of the probability raising principle. We finally combine these two ingredients in PC-like and FCI-like algorithms to construct the summary causal graph. There algorithm are evaluated on several datasets, which shows both their efficacy and efficiency.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 4 of its 39 references that are in the map. First posted on arXiv 21 May 2021.
Builds on
4 of 39 references.
References · 4
- 2017Detecting causal associations in large nonlinear time series datasetscited by 244
- 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 200
- 2013Learning Equivalence Classes of Bayesian Networks Structurescited by 77
- 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
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Citing papers per year: 2023: 5; 2024: 0; 2025: 3; 2026: 1.
9 papers.
- 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
- 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
- 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
- 2023Identifiability of total effects from abstractions of time series causal graphscited by 14
- 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
- 2025Causal Discovery for Irregularly Time Series with Consistency Guaranteescited by 2
- 2025Transforming Causality: Transformer-Based Temporal Causal Discovery with Prior Knowledge Integrationcited by 1
- 2025Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributionscited by 0
- 2026Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systemscited by 0
- 2026Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems18 Aug 2026
- 2025Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions29 Aug 2025
- 2025Transforming Causality: Transformer-Based Temporal Causal Discovery with Prior Knowledge Integration21 Aug 2025
- 2025Causal Discovery for Irregularly Time Series with Consistency Guarantees4 Jul 2025
- 2023Identifiability of total effects from abstractions of time series causal graphs23 Oct 2023
- 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms14 Jun 2023
- 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Data27 Mar 2023
- 2023Causal Discovery from Temporal Data: An Overview and New Perspectives17 Mar 2023
- 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops7 Mar 2023