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.
arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.
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.
arXiv:2510. 16703v3 Announce Type: replace-cross Abstract: The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables.
The paper presents a method for partially identifying counterfactual queries without requiring a fully specified causal graph. By exploiting the topological ordering implied by the query itself, the authors transform the identification problem into a linear programming task, enabling bounds on arbitrary counterfactual and nested counterfactual queries. They demonstrate the tightness of these bounds and illustrate the approach on several case studies, showing its usefulness even when causal knowledge is incomplete.
arXiv:2606. 00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess.
arXiv:2608. 19831v1 Announce Type: new Abstract: Causal Bayesian networks (CBNs) and structural causal models (SCMs) are the dominant frameworks for graphical causal reasoning, but they cannot adequately represent all real-world causal systems.
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.
arXiv:2603. 24333v2 Announce Type: replace-cross Abstract: Recently, Forr\'e (arXiv:2104.
arXiv:2610.19211v1 Announce Type: cross Abstract: While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general suffi...
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.
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which causal graphs remain i...