arXiv Machine Learning
Jul 28
Amortized Bayesian Causal Discovery of Extended Factor Graphs
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
Interpretable Causal Discovery via Causal-Effect Constraints
arXiv:2608. 12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system.
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arXiv AI
Aug 6
SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery
arXiv:2608. 04930v1 Announce Type: cross Abstract: Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data.
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.
arXiv Machine Learning
Jul 7
Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets
arXiv:2607. 04447v1 Announce Type: new Abstract: Local causal discovery is a scalable alternative to global structure learning.
arXiv AI
Jun 3
Causal Preference Elicitation
arXiv:2602. 01483v2 Announce Type: replace-cross Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs).
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
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
Jun 9
Implicit Causal Graph Construction in Text via Chain Discovery
arXiv:2606. 07525v1 Announce Type: cross Abstract: Causal graphs in text are typically populated by observable, predefined events.
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 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
Jul 10
Structure Learning on Clustered Data
arXiv:2607. 08238v1 Announce Type: new Abstract: Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery.
arXiv AI
Jul 13
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.