8 citations · 27 across the 6 of their papers we have counts for
6 papers
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…
Greedy Relaxations of the Sparsest Permutation Algorithm
Wai-Yin Lam, Bryan Andrews, Joseph Ramsey
There has been an increasing interest in methods that exploit permutation reasoning to search for directed acyclic causal models, including the "Ordering Search" of Teyssier and Ko…
Learning Latent Causal Structures with a Redundant Input Neural Network
Jonathan D. Young, Bryan Andrews, Gregory F. Cooper +1
Most causal discovery algorithms find causal structure among a set of observed variables. Learning the causal structure among latent variables remains an important open problem, pa…
FASK with Interventional Knowledge Recovers Edges from the Sachs Model
Joseph Ramsey, Bryan Andrews
We report a procedure that, in one step from continuous data with minimal preparation, recovers the graph found by Sachs et al. \cite{sachs2005causal}, with only a few edges differ…
A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables
Joseph D. Ramsey, Bryan Andrews
We compare Tetrad (Java) algorithms to the other public software packages BNT (Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla" task of recovering DAG structure…