mlsanityData to 14 Oct 2026

Identifiability by common backdoor in summary causal graphs of time series

arXiv 2506.14862 ↗First posted 17 Jun 2025math.ST cs.AI stat.THcited by 2

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Abstract (arXiv)

The identifiability problem for interventions aims at assessing whether the total effect of some given interventions can be written with a do-free formula, and thus be computed from observational data only. We study this problem, considering multiple interventions and multiple effects, in the context of time series when only abstractions of the true causal graph in the form of summary causal graphs are available. We focus in this study on identifiability by a common backdoor set, and establish, for time series with and without consistency throughout time, conditions under which such a set exists. We also provide algorithms of limited complexity to decide whether the problem is identifiable or not.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 17 Jun 2025.

Builds on

6 of 22 references.

References · 6

  1. 2013Strong Completeness and Faithfulness in Bayesian Networkscited by 45
  2. 2016From Deterministic ODEs to Dynamic Structural Causal Modelscited by 32
  3. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  4. 2023Identifiability of total effects from abstractions of time series causal graphscited by 14
  5. 2023Identifiability of Direct Effects from Summary Causal Graphscited by 12
  6. 2013A generalized back-door criterioncited by 9

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 2
  2. 2025On Efficient Adjustment for Micro Causal Effects in Summary Causal Graphscited by 0

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  3. 2026CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
  4. 2025Partially Functional Dynamic Backdoor Diffusion-based Causal Model
  5. 2022Partial Identification with Noisy Covariates: A Robust Optimization Approach
  6. 2025Using Time Structure to Estimate Causal Effects
  7. 2025Causal Time Series Generation via Diffusion Models
  8. 2025Context-Specific Causal Graph Discovery with Unobserved Contexts: Non-Stationarity, Regimes and Spatio-Temporal Patterns