Web: http://arxiv.org/abs/2206.07824

June 17, 2022, 1:10 a.m. | Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev

cs.LG updates on arXiv.org arxiv.org

A common theme in causal inference is learning causal relationships between
observed variables, also known as causal discovery. This is usually a daunting
task, given the large number of candidate causal graphs and the combinatorial
nature of the search space. Perhaps for this reason, most research has so far
focused on relatively small causal graphs, with up to hundreds of nodes.
However, recent advances in fields like biology enable generating experimental
data sets with thousands of interventions followed by rich …

arxiv discovery graphs ml scale

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY