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.
Unsupervised Causal Abstractions Discovery
arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.
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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 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 AI
Jul 14
CDFM: Towards a General-Purpose Causal Discovery Foundation Model
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.
arXiv Machine Learning
Jul 14
Integrating Background Knowledge for Scalable Causal Discovery
arXiv:2607. 10456v1 Announce Type: cross Abstract: Expert background knowledge is often available in practical applications of causal discovery.
arXiv AI
Aug 11
From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
arXiv:2604. 22416v2 Announce Type: replace-cross Abstract: Latent variables pose a fundamental obstacle to both causal discovery and inference.
arXiv AI
Jun 9
Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
arXiv:2606. 08196v1 Announce Type: cross Abstract: We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM).
arXiv Machine Learning
Sep 22
Decoupled Causal Discovery
arXiv:2609.23535v1 Announce Type: new Abstract: Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on...
arXiv Machine Learning
Jun 26
Use What You Know: Causal Foundation Models with Partial Graphs
arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.
arXiv AI
Aug 5
GENESIS: Towards Explainable Causal Discovery
arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.
arXiv Machine Learning
Sep 1
Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions
arXiv:2608.28991v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...
arXiv Machine Learning
Jun 5
Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.