arXiv Machine Learning
Jul 14
Causal Foundation Models with Continuous Treatments
arXiv:2605. 15133v2 Announce Type: replace Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines.
Personalized Causal Recourse: A Human-In-The-Loop Approach
arXiv:2607. 03425v1 Announce Type: new Abstract: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios.
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arXiv AI
Jul 24
Probabilistic Residual Learning for Online Recommendations
arXiv:2607. 20863v1 Announce Type: cross Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items.
Hugging Face Trending Papers
Jul 23
Probabilistic Residual Learning for Online Recommendations
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffer from the black-box nature and computational complexity of the underlying models, making it difficult to systematically enhance their recommendation capabilities.
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 Machine Learning
Jul 31
The Role of Causality in Algorithmic Recourse
arXiv:2607. 28497v1 Announce Type: new Abstract: Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage applications.
arXiv AI
Jul 20
Information-Directed Sampling for Causal Bandits
arXiv:2607. 15577v1 Announce Type: cross Abstract: Causal bandits exploit structural relationships among variables to share information across interventions and accelerate the identification of high-reward decisions.
arXiv AI
Jun 10
Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
arXiv:2606. 10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making.
arXiv Machine Learning
Jun 5
Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applications
arXiv:2606. 06288v1 Announce Type: cross Abstract: Causal representation learning aims to infer the high-level latent causal concepts that give rise to observed low-level measurements.
arXiv AI
Jul 20
Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
arXiv:2607. 15281v1 Announce Type: new Abstract: Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms.
arXiv AI
Jun 2
Transferring Information Across Interventions in Causal Bayesian Optimization
arXiv:2606. 01457v1 Announce Type: new Abstract: Bayesian optimization is a popular way to optimize expensive systems, where every experiment, simulation, or intervention costs time or money.
arXiv AI
Jul 7
MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
arXiv:2603. 25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.
arXiv Machine Learning
Jun 17
Instrumental and Proximal Causal Inference with Gaussian Processes
arXiv:2603. 02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding.