arXiv Machine Learning July 15, 2026 By Jeroen F. Uleman, Loes Crielaard, Leonie K. Elsenburg, Guido A. Veldhuis, Naja Hulvej Rod, Rick Quax, V\'itor V. Vasconcelos

Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty

Read the original on arXiv Machine Learning →

arXiv:2508. 05659v4 Announce Type: replace Abstract: Background: Causal loop diagrams (CLDs) are widely used in health and environmental research to represent hypothesized causal structures underlying complex problems.

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arXiv Machine Learning

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Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs

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.

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arXiv Machine Learning

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Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference

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Causal Reasoning with Bipartite Graphical Causal Models

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By Joris M. Mooij

arXiv AI

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From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond

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By Spyridon Evangelatos, Christos Diou, Georgios Th. Papadopoulos, Evangelos Markakis, Panagiotis Sarigiannidis

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arXiv Machine Learning

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arXiv Machine Learning

2d ago

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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