arXiv AI

What is causal about causal models and representations?

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.

arXiv AI

Sep 4

Symmetries and Causality: Causal Effect Identification Beyond IID Data

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

reinforcement-learning

Hugging Face Trending Papers

Jun 23

An Introduction to Causal Reinforcement Learning

Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i. e.

agents reinforcement-learning

arXiv AI

Jun 24

An Introduction to Causal Reinforcement Learning

arXiv:2606. 24160v1 Announce Type: new Abstract: Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i.

By Elias Bareinboim, Junzhe Zhang, Sanghack Lee

agents reinforcement-learning

arXiv AI

Sep 4

A Computationally Feasible Framework for Causal Probabilistic Explanation

The paper introduces Probabilistic Causal Impact (PCI), a framework that blends actual causality (AC) with Pearl’s probability of necessity and sufficiency to provide tractable, causally grounded explanations. PCI reframes explainability as an estimation problem on a probabilistic causal model, enabling efficient approximation via Monte Carlo sampling. The authors evaluate PCI on synthetic and real-world data, demonstrating consistency with AC, scalability, and applicability to complex continuous systems and large-scale causal machine learning models.

By Rafal Urbaniak, Sam Witty, Daniel Waxman, Andy Zane, Poorva Garg, Emily Bunnapradist, Sankaran Vaidyanathan, Jack Feser, Drew Lehe, Eli Bingham

arXiv Machine Learning

Sep 23

xWhyL: Causal Interactive Learning

arXiv:2609.26037v1 Announce Type: new Abstract: Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for lear...

By Nicholas Tagliapietra, Florian Peter Busch, Moritz Willig, Matej Ze\v{c}evi\'c, Lavdim Halilaj, Juergen Luettin, Kristian Kersting

arXiv Machine Learning

Jun 19

Unsupervised Causal Abstractions Discovery

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.

By Th\'eo Saulus, Simon Lacoste-Julien, Dhanya Sridhar

arXiv AI

Aug 14

A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

arXiv:2608. 13456v1 Announce Type: new Abstract: World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution.

By Avinash Kori, Fabrizio Russo

agents

arXiv Machine Learning

Jul 14

Relaxing Faithfulness with Intervention-Only Causal Discovery

arXiv:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.

By Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler

Hugging Face Trending Papers

Aug 13

A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from a causal perspective across multiple levels of abstraction, ranging from perceptual observations to building a conceptual representation of the structure governing the environment dynamics.

agents

arXiv AI

Aug 21

Causal Reasoning with Bipartite Graphical Causal Models

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.

By Joris M. Mooij

arXiv Machine Learning

Aug 10

Adversarial Causal Intervention Falsification

arXiv:2608. 06427v1 Announce Type: new Abstract: Generative models can reproduce an observational distribution while encoding an incorrect causal structure.

By Mojtaba Eslami

safety

arXiv Machine Learning

Oct 2

CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes

CIDER-FM is a causal foundation model that combines finite observational data with surrogate-interventional datasets to predict target conditional interventional distributions more accurately than using observational data alone. It employs an intervention-aware representation and hierarchical three‑axis attention to integrate information across variables, samples, and experimental regimes. Experiments on synthetic graphs, simulated data, and real‑world Causal Chambers data show that incorporating experimental context improves CID prediction performance.

By Yuche Gao, Arik Reuter, Siyuan Guo, Anish Dhir, Bernhard Sch\"olkopf, Adrian Weller

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