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arXiv Machine Learning

Invariance of Clustering Operations in Causal Effect Identification

The paper studies how clustering variables in causal graphs can simplify inference but may also distort causal relations. It defines an "identification invariant" clustering operation—one that preserves both identifiability and nonidentifiability of causal effects—and presents a broad class of such operations based on conditions involving the c‑components of the original graph. The authors illustrate the practical application of these results in real‑world settings.

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arXiv AI
Oct 12

Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of Criteria

The paper investigates how to determine causal effects when only partial causal information is available, using collections of compatible causal graphs instead of a single fully specified graph. It introduces several formal notions of identifiability for these graph collections and arranges them into a strict hierarchy that clarifies their interrelations. The authors show that a causal effect can be identified across a set of graphs even when no single causal derivation applies uniformly to all of them, thereby exposing gaps and ambiguities in prior work.

By Cl\'ement Yvernes, Marianne Clausel, Emilie Devijver, Eric Gaussier
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arXiv Machine Learning
Jun 18

Clustering and Pruning in Causal Data Fusion

arXiv:2505. 15215v3 Announce Type: replace-cross Abstract: Data fusion, the process of combining observational and experimental data, can enable the identification of causal effects that would otherwise remain non-identifiable.

By Otto Tabell, Santtu Tikka, Juha Karvanen
efficiency
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arXiv Machine Learning
Jul 10

Structure Learning on Clustered Data

arXiv:2607. 08238v1 Announce Type: new Abstract: Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery.

By Ryan Thompson, Matt P. Wand, Veerabhadran Baladandayuthapani
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arXiv Machine Learning
Sep 22

Decoupled Causal Discovery

arXiv:2609.23535v1 Announce Type: new Abstract: Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on...

By Zhengkang Guan, Fei Wu, Kun Kuang
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arXiv AI
Jun 19

Computational Identifiability

arXiv:2606. 19361v1 Announce Type: cross Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available.

By Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun Cho
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arXiv Machine Learning
Oct 9

Score-Based Learning of Cluster DAGs from Interventions

The paper introduces COARSE, a score‑based method for learning cluster directed acyclic graphs (DAGs) from interventional data. COARSE retains the two‑phase structure of prior constraint‑based approaches—first identifying clusters, then edges—but replaces the edge‑learning phase with a score‑based local search under a cluster‑level BIC score, assuming linear Gaussian relationships. The authors prove polynomial‑time complexity and consistency (given correct intervention targets) and demonstrate that COARSE achieves comparable edge recovery to state‑of‑the‑art methods while being up to two orders of magnitude faster, even on dense graphs with hundreds of nodes.

By Gaetano Tedesco (University of Amsterdam), Alex Markham (University of Copenhagen)
benchmarks
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