arXiv Machine Learning June 9, 2026 By Jifan Zhang, Michelle M. Li, Elena Zheleva

Causal Representation Learning from Network Data

Read the original on arXiv Machine Learning →

arXiv:2509. 01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 3

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies

arXiv:2401. 04890v2 Announce Type: replace-cross Abstract: This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed auxiliary variables and/or past latent factors.

By S\'ebastien Lachapelle, Pau Rodr\'iguez L\'opez, Yash Sharma, Katie Everett, R\'emi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

efficiency

arXiv Machine Learning

Aug 11

CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization

arXiv:2603. 24304v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios.

By Bowen Lu, Liangqiang Yang, Teng Li, Kun Zhang

diffusion benchmarks

arXiv Machine Learning

1d 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

arXiv AI

Jun 24

RetiSEM: Generalising Causal Models for Fragmented Biomedical Data

arXiv:2606. 24488v1 Announce Type: cross Abstract: Learning causal models from fragmented biomedical data is challenging because clinical, molecular, and imaging variables are often incomplete or not jointly observed.

By Inam Ullah, Imran Razzak, Shoaib Jameel

multimodal benchmarks

arXiv Machine Learning

Jul 10

Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions

arXiv:2603. 02204v2 Announce Type: replace Abstract: Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example.

By Amir Asiaee, Kavey Aryan, James P. Long

arXiv AI

Jun 17

FoundCause: Causal Discovery with Latent Confounders from Observational Data

arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.

By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

llms

One email a morning, machine-written

Related stories

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies

CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization

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

RetiSEM: Generalising Causal Models for Fragmented Biomedical Data

Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions

FoundCause: Causal Discovery with Latent Confounders from Observational Data