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Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions

arXiv 2508.21742 ↗First posted 29 Aug 2025cs.AI stat.MEcited by 0

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Abstract (arXiv)

Understanding causal relations between temporal variables is a central challenge in time series analysis, particularly when the full causal structure is unknown. Even when the full causal structure cannot be fully specified, experts often succeed in providing a high-level abstraction of the causal graph, known as a summary causal graph, which captures the main causal relations between different time series while abstracting away micro-level details. In this work, we present conditions that guarantee the orientability of micro-level edges between temporal variables given the background knowledge encoded in a summary causal graph and assuming having access to a faithful and causally sufficient distribution with respect to the true unknown graph. Our results provide theoretical guarantees for edge orientation at the micro-level, even in the presence of cycles or bidirected edges at the macro-level. These findings offer practical guidance for leveraging SCGs to inform causal discovery in complex temporal systems and highlight the value of incorporating expert knowledge to improve causal inference from observational time series data.

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References · 15

  1. 2013Direct and Indirect Effectscited by 280
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  3. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
  4. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
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  6. 2013Learning Equivalence Classes of Bayesian Networks Structurescited by 77
  7. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  8. 2023Foundations of Causal Discovery on Groups of Variablescited by 22
  9. 2023Case Studies of Causal Discovery from IT Monitoring Time Seriescited by 14
  10. 2023Identifiability of total effects from abstractions of time series causal graphscited by 14
  11. 2023Identifiability of Direct Effects from Summary Causal Graphscited by 12
  12. 2022Vector causal inference between two groups of variablescited by 11
3 more
  1. 2021Entropy-based Discovery of Summary Causal Graphs in Time Seriescited by 9
  2. 2023Do we become wiser with time? On causal equivalence with tiered background knowledgecited by 9
  3. 2024Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confoundingcited by 9

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