mlsanityData to 7 Oct 2026

Foundations of Causal Discovery on Groups of Variables

arXiv 2306.07047 ↗First posted 12 Jun 2023stat.ME math.ST stat.THcited by 20

Not on the map. Why?

Abstract (arXiv)

Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models. In this work, we focus on widely employed assumptions for causal discovery when objects of interest are (multivariate) groups of random variables rather than individual (univariate) random variables, as is the case in a variety of problems in scientific domains such as climate science or neuroscience. If the group-level causal models are derived from partitioning a micro-level model into groups, we explore the relationship between micro and group-level causal discovery assumptions. We investigate the conditions under which assumptions like Causal Faithfulness hold or fail to hold. Our analysis encompasses graphical causal models that contain cycles and bidirected edges. We also discuss grouped time series causal graphs and variants thereof as special cases of our general theoretical framework. Thereby, we aim to provide researchers with a solid theoretical foundation for the development and application of causal discovery methods for variable groups.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2023.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 7; 2026: 5.

20 papers.

Sort:

  1. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  2. 2024Learning Causal Abstractions of Linear Structural Causal Modelscited by 9
  3. 2024Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confoundingcited by 8
  4. 2024Aligning Graphical and Functional Causal Abstractionscited by 6
  5. 2024Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterioncited by 5
  6. 2026Structural Causal Bottleneck Modelscited by 4
  7. 2025Identifying Macro Causal Effects in a C-DMG over ADMGscited by 3
  8. 2025Nonlinear Causal Discovery for Grouped Datacited by 3
  9. 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clusteringcited by 3
  10. 2024Systems with Switching Causal Relations: A Meta-Causal Perspectivecited by 2
  11. 2025Identifying Macro Causal Effects in C-DMGs over DMGscited by 2
  12. 2026Coarsening Causal DAG Modelscited by 2
8 more
  1. 2024Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphscited by 1
  2. 2024Causal reasoning in difference graphscited by 1
  3. 2025Identifiability in Causal Abstractions: A Hierarchy of Criteriacited by 1
  4. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 1
  5. 2025Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributionscited by 0
  6. 2026Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variablescited by 0
  7. 2026From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variablescited by 0
  8. 2026Joint Causal Structure and Cluster Discovery Using Variational Inferencecited by 0