Bryan Andrews

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 17

Top Topics

Conditional Independence

Causal Discovery

Bayesian Networks

Top Venues

Artif. Intell. Medicine

Publications

Bryan Andrews, Erich Kummerfeld
Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method. CoRR (2024)
Bryan Andrews, Joseph D. Ramsey, Ruben Sanchez-Romero, Jazmin Camchong, Erich Kummerfeld
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees. NeurIPS (2023)
Bryan Andrews, Joseph D. Ramsey, Ruben Sanchez-Romero, Jazmin Camchong, Erich Kummerfeld
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees. CoRR (2023)
Eric Rawls, Bryan Andrews, Kelvin Lim, Erich Kummerfeld
Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study. CoRR (2023)
Joseph D. Ramsey, Bryan Andrews
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search. CAWS (2023)
Bryan Andrews, Chirayu Wongchokprasitti, Shyam Visweswaran, Chirag M. Lakhani, Chirag J. Patel, Gregory F. Cooper
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. Artif. Intell. Medicine 139 (2023)
Joseph D. Ramsey, Bryan Andrews
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search. CoRR (2023)
Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey
Greedy Relaxations of the Sparsest Permutation Algorithm. CoRR (2022)
Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey
Greedy relaxations of the sparsest permutation algorithm. UAI (2022)
Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes
The m-connecting imset and factorization for ADMG models. CoRR (2022)
Bryan Andrews
On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge. AISTATS (2020)
Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu
Learning Latent Causal Structures with a Redundant Input Neural Network. CD@KDD (2020)
Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu
Learning Latent Causal Structures with a Redundant Input Neural Network. CoRR (2020)
Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper
Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types. CD@KDD (2019)
Joseph D. Ramsey, Bryan Andrews
FASK with Interventional Knowledge Recovers Edges from the Sachs Model. CoRR (2018)
Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper
Scoring Bayesian networks of mixed variables. Int. J. Data Sci. Anal. 6 (1) (2018)
Joseph D. Ramsey, Bryan Andrews
A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables. CoRR (2017)