New Causal Discovery Model LRQS Introduced on arXiv

Researchers have introduced Low-Rank Quantile Surfaces (LRQS), a novel bivariate causal model designed to improve causal discovery. LRQS extends existing models by allowing for unknown monotone transformations of conditional quantile surfaces that can be decomposed into a low-rank structure. This approach is particularly effective in scenarios where conditional distributional shapes or observation distortions exceed standard location-scale assumptions, as demonstrated by experiments on synthetic and benchmark datasets. AI

IMPACT Introduces a new statistical method that could enhance causal inference capabilities in AI research.

RANK_REASON The cluster contains a research paper detailing a new methodology for causal discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• Connected Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• Influence Flower
• Litmaps
• Low-Rank Quantile Surfaces
• LRQS
• ScienceCast
• scite Smart Citations

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →