arXiv Machine Learning July 13, 2026 By Aniq Ur Rahman

Estimation-Prediction Tradeoff in Causal Probabilistic Temporal Graphs

Read the original on arXiv Machine Learning →

arXiv:2606. 28225v2 Announce Type: replace Abstract: Temporal link prediction (TLP) is typically evaluated by predictive performance on unseen edges, but this criterion can conflate predictive accuracy with recovery of the underlying causal mechanism.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 29

Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs

arXiv:2606. 28225v1 Announce Type: new Abstract: Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty.

By Aniq Ur Rahman

benchmarks

arXiv Machine Learning

Jun 5

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.

By Hazhir Aliahmadi, Irina Babayan, Greg van Anders

arXiv AI

Jul 13

How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding

arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.

By Debargha Ghosh, Silja Renooij, Anna Kononova

arXiv Machine Learning

Jun 26

Use What You Know: Causal Foundation Models with Partial Graphs

arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.

By Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Sch\"olkopf

arXiv AI

Jun 3

Causal Preference Elicitation

arXiv:2602. 01483v2 Announce Type: replace-cross Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs).

By Edwin V. Bonilla, He Zhao, Daniel M. Steinberg

benchmarks

arXiv AI

Jun 3

CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery

arXiv:2606. 03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equivalence classes and sensitivity to finite sample sizes.

By Bo Peng, Kaiwen Wu, Sirui Chen, Zhiheng Wang, Yu Qiao, Chaochao Lu

llms

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Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs

Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs

How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding

Use What You Know: Causal Foundation Models with Partial Graphs

Causal Preference Elicitation

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