mlsanityData to 14 Oct 2026

Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion

arXiv 2406.05805 ↗First posted 9 Jun 2024stat.ME cs.AIcited by 5

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Abstract (arXiv)

Conducting experiments to estimate total effects can be challenging due to cost, ethical concerns, or practical limitations. As an alternative, researchers often rely on causal graphs to determine whether these effects can be identified from observational data. Identifying total effects in fully specified causal graphs has received considerable attention, with Pearl's front-door criterion enabling the identification of total effects in the presence of latent confounding even when no variable set is sufficient for adjustment. However, specifying a complete causal graph is challenging in many domains. Extending these identifiability results to partially specified graphs is crucial, particularly in dynamic systems where causal relationships evolve over time. This paper addresses the challenge of identifying total effects using a specific and well-known partially specified graph in dynamic systems called a summary causal graph, which does not specify the temporal lag between causal relations and can contain cycles. In particular, this paper presents sufficient graphical conditions for identifying total effects from observational data, even in the presence of cycles and latent confounding, and when no variable set is sufficient for adjustment.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 10 of its 43 references that are in the map. First posted on arXiv 9 Jun 2024.

Builds on

10 of 43 references.

References · 10

  1. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  2. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
  3. 2022Causal Effect Identification in Cluster DAGscited by 36
  4. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  5. 2023Foundations of Causal Discovery on Groups of Variablescited by 22
  6. 2023Case Studies of Causal Discovery from IT Monitoring Time Seriescited by 14
  7. 2023Identifiability of total effects from abstractions of time series causal graphscited by 14
  8. 2023Identifiability of Direct Effects from Summary Causal Graphscited by 12
  9. 2013A generalized back-door criterioncited by 9
  10. 2016Identifiability and Transportability in Dynamic Causal Networkscited by 7

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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2025Identifying Macro Causal Effects in a C-DMG over ADMGscited by 4
  2. 2025Identifying Macro Causal Effects in C-DMGs over DMGscited by 3
  3. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 2
  4. 2024Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphscited by 1
  5. 2025On Efficient Adjustment for Micro Causal Effects in Summary Causal Graphscited by 0