arXiv Machine Learning By Amir Asiaee, Kavey Aryan, James P. Long

Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions

Read the original on arXiv Machine Learning →

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.

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
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
arXiv Machine Learning
Jun 9

Causal Representation Learning from Network Data

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.

By Jifan Zhang, Michelle M. Li, Elena Zheleva
arXiv Machine Learning
Sep 11

CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

CausalArena is a new benchmark designed to evaluate causal discovery methods in the era of foundation models. It unifies synthetic structural causal models (SCMs), semantically grounded SCMs, and formula‑grounded SCMs, while also including real‑world datasets for external validation. Experiments show that performance rankings vary widely across different SCM families and protocols, indicating that strong results on one benchmark do not necessarily transfer to others.

By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye