1 code implementation • 21 May 2024 • Bryan Andrews, Erich Kummerfeld
We propose a new simulation design for generating linear models for directed acyclic graphs (DAGs): the DAG-adaptation of the Onion (DaO) method.
no code implementations • 20 Dec 2023 • Eric Rawls, Bryan Andrews, Kelvin Lim, Erich Kummerfeld
Designing studies that apply causal discovery requires navigating many researcher degrees of freedom.
1 code implementation • 26 Oct 2023 • Bryan Andrews, Joseph Ramsey, Ruben Sanchez-Romero, Jazmin Camchong, Erich Kummerfeld
However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables -- for instance, recovering brain networks from fMRI data.
2 code implementations • 13 Aug 2023 • Joseph D. Ramsey, Bryan Andrews
With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.
no code implementations • 18 Jul 2022 • Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes
The m-connecting imset and factorization criterion provide two new statistical tools for learning and inference with ADMG models.
1 code implementation • 11 Jun 2022 • 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 Kohler and GSP of Solus, Wang and Uhler.
no code implementations • 29 Mar 2020 • Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu
We developed a deep learning model, which we call a redundant input neural network (RINN), with a modified architecture and a regularized objective function to find causal relationships between input, hidden, and output variables.
no code implementations • 6 May 2018 • 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 different.
no code implementations • 13 Sep 2017 • 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 to the extent possible from data generated recursively from linear, Gaussian structure equation models (SEMs) with no latent variables, for random graphs, with no additional knowledge of variable order or adjacency structure, and without additional specification of intervention information.