7 citations · 10 across the 3 of their papers we have counts for
3 papers
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Bryan Andrews, Joseph Ramsey, Ruben Sanchez-Romero +2
Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of l…
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search
Joseph D. Ramsey, Bryan Andrews
We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been und…
The m-connecting imset and factorization for ADMG models
Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson +1
Directed acyclic graph (DAG) models have become widely studied and applied in statistics and machine learning -- indeed, their simplicity facilitates efficient procedures for learn…