arXiv:2606. 05029v1 Announce Type: new Abstract: Controlled experiments are the backbone of machine learning research, but at the scale of modern foundation models, they have become prohibitively expensive.
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...
CausalProfiler is a synthetic benchmark generator designed to evaluate causal machine learning (Causal ML) methods more rigorously and transparently. It randomly samples causal models, data, queries, and ground truths based on explicit design choices across observation, intervention, and counterfactual reasoning levels, providing coverage guarantees and transparent assumptions. The authors demonstrate its utility by testing several state‑of‑the‑art methods under diverse conditions, both within and outside the identification regime, highlighting the insights CausalProfiler can reveal.
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.
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.
arXiv:2601. 20819v2 Announce Type: replace-cross Abstract: Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science.
arXiv:2607. 21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice.
LeakScale is an interventional framework that estimates the causal effect of benchmark exposure on model performance. By creating new executable tasks that require private, family‑specific information and controlling access to that information, LeakScale measures the control‑adjusted change in accuracy. Across 2,048 families, two model families, two domains, and 262,144 generations, exposure consistently improves accuracy by 7.17 to 27.31 percentage points, separating the question of whether contact occurred from how much a score depends on it.
LeakScale is an interventional framework that estimates the causal effect of benchmark exposure on model performance. By creating new executable tasks that require private, family‑specific information not present in the public task, LeakScale controls access to that information and measures the resulting change in accuracy. Across 2,048 families, two model families, two domains, and 262,144 generations, exposure consistently improves accuracy, with gains ranging from +7.17 to +27.31 percentage points, thereby distinguishing whether benchmark contact occurred from how strongly a reported score depends on it.
arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.
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: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.
arXiv:2606. 23741v1 Announce Type: cross Abstract: Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making.