The paper investigates why causal Bayesian networks are labeled as causal, noting that interventions are defined within the model while predictions refer to real-world actions. It introduces a formal framework that separates the causal model from the data-generating process and formalizes how actions map to interventions. The authors prove an impossibility result showing that no non‑circular interpretation can satisfy all desirable properties, and they explore the consequences for falsification, causal discovery, representation learning, and abstraction.
By Frederik Hytting J{\o}rgensen, Luigi Gresele, Sebastian Weichwald
arXiv:2607. 22910v1 Announce Type: new Abstract: Pearl's structural causal model (SCM) framework, built on directed acyclic graphs (DAGs) and the do-calculus, is the dominant formal language for causal reasoning.
By Sergei V. Kalinin
arXiv:2609.06941v1 Announce Type: new
Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundatio...
By Haohao Zhou
The paper introduces a formal framework that uses symmetries in data to keep causal mechanisms invariant, providing a simple and general mathematical language for causal reasoning. It outlines how to describe models and queries, and presents strategies for rigorously identifying causal effects from data within this framework. The approach reproduces known results for IID data and extends causal analysis to non‑IID settings, complex queries beyond do‑ or soft‑interventions, and incorporates missing data, transfer, and robustness considerations.
By Martin Rabel, Jakob Runge
The paper introduces CausalBridge, a framework that uses causal structure to assign semantics to unnamed variables in numerical measurements. By discovering a causal graph from the data and solving for variable embeddings constrained by this graph, the method aligns variable meanings with a language model, outperforming association‑based approaches. Experiments on questionnaires and robotics scenarios show high accuracy even when most variable names are masked, enabling rapid and cost‑effective system naming.
By Shuhao Zhang, Xuran Zhou, Han Guo, Pengtao Xie, Yujia Zheng
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
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.
By Hazhir Aliahmadi, Irina Babayan, Greg van Anders
arXiv:2608.28991v1 Announce Type: cross
Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...
By Haijie Xu, Chen Zhang
arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.
By Abhinav Thorat, Ravi Kumar Kolla, Vishak K Bhat, Harsh Vardhan Singh Chauhan, Niranjan Pedanekar
arXiv:2608. 15645v1 Announce Type: new Abstract: Transporting a causal conclusion from a source study population to a target one is a fundamental problem in causal inference.
By Yorgos Felekis, Paris Giampouras, Fabio Massimo Zennaro, Theodoros Damoulas
arXiv:2607. 00267v1 Announce Type: cross Abstract: A central goal of science is to produce valid explanations of complex systems: high-level causal accounts that faithfully reflect the behavior of lower-level mechanisms.
By Maxime M\'eloux, Tiago Pimentel, Fran\c{c}ois Portet, Maxime Peyrard
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.
By Erik Jahn, Dominik Janzing