arXiv AI
Jun 9
Instrumented data for causal scientific machine learning
arXiv:2606. 07865v1 Announce Type: cross Abstract: Scientific machine learning is limited less by model size than by the data it is trained on.
Sanity Checking Causal Representation Learning on a Simple Real-World System
The paper evaluates causal representation learning (CRL) methods on a controlled optical experiment where the true causal factors are known. All selected methods fail to reliably recover these factors, even when the data-generating process is simplified to a synthetic version. The study highlights reproducibility issues and the importance of mixing function assumptions, underscoring the gap between theoretical promise and practical performance of CRL techniques.
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arXiv AI
Jul 7
Additive Causal Construction for Transferable and Reconfigurable Cross-System Learning in Multi-Source Image Fusion
arXiv:2607. 02572v1 Announce Type: cross Abstract: In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems.
arXiv Machine Learning
Jun 5
EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
arXiv:2606. 05942v1 Announce Type: cross Abstract: Neural network (NN)-based nonlinear causal discovery methods recover DAG structure but leave each causal mechanism as a black box.
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.
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.
arXiv Machine Learning
Jul 14
Relaxing Faithfulness with Intervention-Only Causal Discovery
arXiv:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.
arXiv AI
Aug 20
CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning
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.
arXiv AI
Jul 3
Causal Explanations for Image Classifiers
arXiv:2411. 08875v4 Announce Type: replace Abstract: Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find them.
arXiv Statistics ML
2d ago
CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
CausalCompass is a benchmark framework that evaluates the robustness of time‑series causal discovery (TSCD) methods when modeling assumptions are violated. The authors benchmark several TSCD algorithms across eight assumption‑violation scenarios, finding that no single method dominates all settings but deep learning approaches generally perform best. They also analyze hyperparameter sensitivity, conduct ablation studies, and highlight the impact of preprocessing on methods like NTS‑NOTEARS.
Hugging Face Trending Papers
Sep 10
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
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 AI
Sep 30
Demistifying Data and Simulator Assumptions in Supervised Causal Discovery
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...
arXiv AI
Oct 21
Causal Discovery for Linear Models with Latent Confounding via Multi-domain Identifiable Representation Learning
arXiv:2610.28134v1 Announce Type: cross Abstract: Causal discovery with latent confounding is particularly challenging, but a common setting in real-world data. In this work, we focus on linear causa...