Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
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Abstract (arXiv)
The identifiability problem for interventions aims at assessing whether the total causal effect can be written with a do-free formula, and thus be estimated from observational data only. We study this problem, considering multiple interventions, in the context of time series when only an abstraction of the true causal graph, in the form of a summary causal graph, is available. We propose in particular both necessary and sufficient conditions for the adjustment criterion, which we show is complete in this setting, and provide a pseudo-linear algorithm to decide whether the query is identifiable or not.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 17 Jun 2025.
Builds on
5 of 22 references.
References · 5
- 2022Causal Effect Identification in Cluster DAGscited by 36
- 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
- 2023Identifiability of Direct Effects from Summary Causal Graphscited by 12
- 2016Identifiability and Transportability in Dynamic Causal Networkscited by 7
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.