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
CausalArena is a unified, evolvable benchmark designed to evaluate causal discovery methods across diverse structural causal models (SCMs). It incorporates synthetic SCMs for controlled structural variation, semantic operational SCMs for human-auditable environments, and formula-grounded SCMs to test discovery under explicit scientific mechanisms, along with real-world datasets for external validity. Experiments show that performance rankings vary significantly across SCM families and protocols, indicating that strong results on one benchmark do not generalize to others, especially in the context of causal discovery foundation models.
arXiv:2609.36881v1 Announce Type: new
Abstract: Causal foundation models (CFMs) pre-trained on data generated from various structural causal models (SCMs) have been proposed for estimating causal eff...
By Heejin Jung, Gyeongdeok Seo, Hoyoon Byun, Joseph Lee, Kyungwoo Song
TabCausal is a causal discovery foundation model that learns to map datasets directly to causal graphs by pretraining across diverse causal environments. It uses a dynamic task construction strategy to expose the model to varied graph priors, mechanisms, noise models, dimensions, sample sizes, and intervention regimes, improving transferability from observational and mixed‑interventional data. On large synthetic benchmarks and a new protocol‑guided semantic benchmark, TabCausal outperforms many classical baselines and shows robust structure recovery, especially when interventional evidence is available.
By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye
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: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:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui
arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.
By Wooseok Ha, Yuansi Chen
arXiv:2609.37446v1 Announce Type: new
Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pai...
By Pingchuan Ma, Rui Ding, Bojun Huang, Shuai Wang
Causal Foundation Models (CFMs) are pretrained neural networks designed to estimate causal quantities—such as the average treatment effect—across new datasets using in‑context learning, eliminating the need for bespoke pipelines or model updates. The paper introduces CFMs, reviews foundational concepts in causal inference and machine learning, and provides practical code examples and Jupyter notebooks to illustrate their application.
By Christopher Stith, Hossein Rahmani, Jesse C. Cresswell
arXiv:2605. 15133v2 Announce Type: replace Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines.
By Christopher Stith, Medha Barath, Vahid Balazadeh, Jesse C. Cresswell, Rahul G. Krishnan
arXiv:2606. 03251v1 Announce Type: new Abstract: In nature, events that affect some individuals or groups but not others constitute an implicit intervention and are known as natural experiments.
By Gautam Gare, John Galeotti, Michael Mozer, Deva Ramanan, Nan Rosemary Ke