Bryan Andrews

22 papers A* 1A 2Journal 15Unranked 3
YearRankTypeTitle / Venue / Authors
2025 J jnl
An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts.
CoRR
Ritwick Banerjee, Bryan Andrews, Erich Kummerfeld
2025 J jnl
Efficient Latent Variable Causal Discovery: Combining Score Search and Targeted Testing.
CoRR
Joseph D. Ramsey, Bryan Andrews
2025 J jnl
Scalable Causal Discovery from Recursive Nonlinear Data via Truncated Basis Function Scores and Tests.
CoRR
Joseph D. Ramsey, Bryan Andrews
2024 J jnl
Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method.
CoRR
Bryan Andrews, Erich Kummerfeld
2024 J jnl
Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth.
CoRR
Joseph D. Ramsey, Bryan Andrews, Peter Spirtes
2023 J jnl
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
Bryan Andrews, Chirayu Wongchokprasitti, Shyam Visweswaran, Chirag M. Lakhani, Chirag J. Patel, Gregory F. Cooper
2023 — ed.
Causal Analysis Workshop Series, CAWS 2023, Virtual Event, August 14, 2023.
CAWS
Erich Kummerfeld, Sisi Ma, Eric Rawls, Bryan Andrews
2023 J jnl
Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study.
CoRR
Eric Rawls, Bryan Andrews, Kelvin Lim, Erich Kummerfeld
2023 A* conf
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees.
NeurIPS
Bryan Andrews, Joseph D. Ramsey, Ruben Sanchez-Romero, Jazmin Camchong, Erich Kummerfeld
2023 J jnl
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees.
CoRR
Bryan Andrews, Joseph D. Ramsey, Ruben Sanchez-Romero, Jazmin Camchong, Erich Kummerfeld
2023 — conf
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search.
CAWS
Joseph D. Ramsey, Bryan Andrews
2023 J jnl
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search.
CoRR
Joseph D. Ramsey, Bryan Andrews
2022 J jnl
Greedy Relaxations of the Sparsest Permutation Algorithm.
CoRR
Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey
2022 A conf
Greedy relaxations of the sparsest permutation algorithm.
UAI
Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey
2022 J jnl
The m-connecting imset and factorization for ADMG models.
CoRR
Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes
2020 — conf
Learning Latent Causal Structures with a Redundant Input Neural Network.
CD@KDD
Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu
2020 J jnl
Learning Latent Causal Structures with a Redundant Input Neural Network.
CoRR
Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu
2020 A conf
On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge.
AISTATS
Bryan Andrews
2019 — conf
Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types.
CD@KDD
Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper
2018 J jnl
FASK with Interventional Knowledge Recovers Edges from the Sachs Model.
CoRR
Joseph D. Ramsey, Bryan Andrews
2018 J jnl
Scoring Bayesian networks of mixed variables.
Int. J. Data Sci. Anal.
Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper
2017 J jnl
A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables.
CoRR
Joseph D. Ramsey, Bryan Andrews