Bryan Andrews

According to our database, Bryan Andrews authored at least 14 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth.

Joseph D. Ramsey, Bryan Andrews, Peter Spirtes

CoRR, 2024

Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method.

Bryan Andrews, Erich Kummerfeld

CoRR, 2024

2023

A new method for estimating the probability of causal relationships from observational data: Application to the study of the short-term effects of air pollution on cardiovascular and respiratory disease.

Bryan Andrews, Chirayu Wongchokprasitti, Shyam Visweswaran, Chirag M. Lakhani, Chirag J. Patel, Gregory F. Cooper

Artif. Intell. Medicine, May, 2023

Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study.

Eric Rawls, Bryan Andrews, Kelvin Lim, Erich Kummerfeld

CoRR, 2023

Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees.

Bryan Andrews, Joseph D. Ramsey, Ruben Sanchez-Romero, Jazmin Camchong, Erich Kummerfeld

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search.

Joseph D. Ramsey, Bryan Andrews

Proceedings of the Causal Analysis Workshop Series, 2023

2022

The m-connecting imset and factorization for ADMG models.

Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes

CoRR, 2022

Greedy relaxations of the sparsest permutation algorithm.

Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2020

Learning Latent Causal Structures with a Redundant Input Neural Network.

Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu

Proceedings of the 2020 KDD Workshop on Causal Discovery (CD@KDD 2020), 2020

On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge.

Bryan Andrews

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types.

Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper

Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, 2019

2018

Scoring Bayesian networks of mixed variables.

Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper

Int. J. Data Sci. Anal., 2018

FASK with Interventional Knowledge Recovers Edges from the Sachs Model.

Joseph D. Ramsey, Bryan Andrews

CoRR, 2018

2017

A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables.

Joseph D. Ramsey, Bryan Andrews

CoRR, 2017