mlsanityData to 14 Oct 2026

Identifying Macro Causal Effects in a C-DMG over ADMGs

arXiv 2504.01551 ↗First posted 2 Apr 2025cs.AIcited by 4

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Abstract (arXiv)

Causal effect identification using causal graphs is a fundamental challenge in causal inference. While extensive research has been conducted in this area, most existing methods assume the availability of fully specified directed acyclic graphs or acyclic directed mixed graphs. However, in complex domains such as medicine and epidemiology, complete causal knowledge is often unavailable, and only partial information about the system is accessible. This paper focuses on causal effect identification within partially specified causal graphs, with particular emphasis on cluster-directed mixed graphs (C-DMGs) which can represent many different acyclic directed mixed graphs (ADMGs). These graphs provide a higher-level representation of causal relationships by grouping variables into clusters, offering a more practical approach for handling complex systems. Unlike fully specified ADMGs, C-DMGs can contain cycles, which complicate their analysis and interpretation. Furthermore, their cluster-based nature introduces new challenges, as it gives rise to two distinct types of causal effects: macro causal effects and micro causal effects, each with different properties. In this work, we focus on macro causal effects, which describe the effects of entire clusters on other clusters. We establish that the do-calculus is both sound and complete for identifying these effects in C-DMGs over ADMGs when the cluster sizes are either unknown or of size greater than one. Additionally, we provide a graphical characterization of non-identifiability for macro causal effects in these graphs.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 2 Apr 2025.

Builds on

12 of 25 references.

References · 12

  1. 2022Causal Effect Identification in Cluster DAGscited by 36
  2. 2018Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounderscited by 35
  3. 2017Markov Properties for Graphical Models with Cycles and Latent Variablescited by 32
  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. 2019Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Biascited by 17
  7. 2023Identifiability of total effects from abstractions of time series causal graphscited by 14
  8. 2021Clustering and Structural Robustness in Causal Diagramscited by 12
  9. 2023Identifiability of Direct Effects from Summary Causal Graphscited by 12
  10. 2013A generalized back-door criterioncited by 9
  11. 2024Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confoundingcited by 9
  12. 2024Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterioncited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Identifying Macro Causal Effects in C-DMGs over DMGscited by 3
  2. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 2
  3. 2026From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variablescited by 0
  4. 2026Invariance of Clustering Operations in Causal Effect Identificationcited by 0