mlsanityData to 14 Oct 2026

Identifiability of total effects from abstractions of time series causal graphs

arXiv 2310.14691 ↗First posted 23 Oct 2023math.ST cs.AI stat.THcited by 14

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Abstract (arXiv)

We study the problem of identifiability of the total effect of an intervention from observational time series in the situation, common in practice, where one only has access to abstractions of the true causal graph. We consider here two abstractions: the extended summary causal graph, which conflates all lagged causal relations but distinguishes between lagged and instantaneous relations, and the summary causal graph which does not give any indication about the lag between causal relations. We show that the total effect is always identifiable in extended summary causal graphs and provide sufficient conditions for identifiability in summary causal graphs. We furthermore provide adjustment sets allowing to estimate the total effect whenever it is identifiable.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 12 of its 33 references that are in the map. First posted on arXiv 23 Oct 2023.

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12 of 33 references.

References · 12

  1. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  2. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  3. 2022Causal Effect Identification in Cluster DAGscited by 36
  4. 2016From Deterministic ODEs to Dynamic Structural Causal Modelscited by 32
  5. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  6. 2021Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variablescited by 16
  7. 2023Case Studies of Causal Discovery from IT Monitoring Time Seriescited by 14
  8. 2023Identifiability of Direct Effects from Summary Causal Graphscited by 12
  9. 2013A generalized back-door criterioncited by 9
  10. 2021Entropy-based Discovery of Summary Causal Graphs in Time Seriescited by 9
  11. 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
  12. 2016Identifiability and Transportability in Dynamic Causal Networkscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 2.

14 papers.

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  1. 2024Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confoundingcited by 9
  2. 2024Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterioncited by 5
  3. 2025Identifying Macro Causal Effects in a C-DMG over ADMGscited by 4
  4. 2025Complete Characterization for Adjustment in Summary Causal Graphs of Time Seriescited by 3
  5. 2025Identifying Macro Causal Effects in C-DMGs over DMGscited by 3
  6. 2024Causal reasoning in difference graphscited by 2
  7. 2025Identifiability by common backdoor in summary causal graphs of time seriescited by 2
  8. 2025Identifiability in Causal Abstractions: A Hierarchy of Criteriacited by 2
  9. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 2
  10. 2024Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphscited by 1
  11. 2025Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributionscited by 0
  12. 2025On Efficient Adjustment for Micro Causal Effects in Summary Causal Graphscited by 0
2 more
  1. 2026Teleological Inference in Structural Causal Models via Intentional Interventionscited by 0
  2. 2026Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of Criteriacited by 0