mlsanityData to 7 Oct 2026

Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding

arXiv 2407.07934 ↗First posted 10 Jul 2024stat.ME cs.AIcited by 8

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Abstract (arXiv)

Understanding causal relations in dynamic systems is essential in epidemiology. While causal inference methods have been extensively studied, they often rely on fully specified causal graphs, which may not always be available in complex dynamic systems. Partially specified causal graphs, and in particular summary causal graphs (SCGs), provide a simplified representation of causal relations between time series when working spacio-temporal data, omitting temporal information and focusing on causal structures between clusters of of temporal variables. Unlike fully specified causal graphs, SCGs can contain cycles, which complicate their analysis and interpretation. In addition, their cluster-based nature introduces new challenges concerning the types of queries of interest: macro queries, which involve relationships between clusters represented as vertices in the graph, and micro queries, which pertain to relationships between variables that are not directly visible through the vertices of the graph. In this paper, we first clearly distinguish between macro conditional independencies and micro conditional independencies and between macro total effects and micro total effects. Then, we demonstrate the soundness and completeness of the d-separation to identify macro conditional independencies in SCGs. Furthermore, we establish that the do-calculus is sound and complete for identifying macro total effects in SCGs. Finally, we give a graphical characterization for the non-identifiability of macro total effects in SCGs.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 12 of its 41 references that are in the map. First posted on arXiv 10 Jul 2024.

Builds on

12 of 41 references.

References · 12

  1. 2017Causal Consistency of Structural Equation Modelscited by 114
  2. 2018Abstracting Causal Modelscited by 104
  3. 2018Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounderscited by 35
  4. 2017Markov Properties for Graphical Models with Cycles and Latent Variablescited by 32
  5. 2015Multi-Level Cause-Effect Systemscited by 30
  6. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  7. 2023Foundations of Causal Discovery on Groups of Variablescited by 20
  8. 2019Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Biascited by 16
  9. 2023Identifiability of total effects from abstractions of time series causal graphscited by 13
  10. 2023Identifiability of Direct Effects from Summary Causal Graphscited by 12
  11. 2023When does the ID algorithm fail?cited by 4
  12. 2024Causal Inference on Process Graphs, Part II: Causal Structure and Effect Identificationcited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 7.

8 papers.

  1. 2025Identifying Macro Causal Effects in a C-DMG over ADMGscited by 3
  2. 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clusteringcited by 3
  3. 2025Note on the identification of total effect in Cluster-DAGs with cyclescited by 2
  4. 2025Identifying Macro Causal Effects in C-DMGs over DMGscited by 2
  5. 2024Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphscited by 1
  6. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 1
  7. 2025Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributionscited by 0
  8. 2025On Efficient Adjustment for Micro Causal Effects in Summary Causal Graphscited by 0