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2020 – today
- 2024
[i10]Bryan Andrews, Erich Kummerfeld:
Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method. CoRR abs/2405.13100 (2024)- [i9]Joseph D. Ramsey, Bryan Andrews, Peter Spirtes:
Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth. CoRR abs/2409.20187 (2024) - 2023
- [j2]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: 102546 (2023) - [c6]Joseph D. Ramsey, Bryan Andrews:
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search. CAWS 2023: 40-51 - [c5]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 - [e1]Erich Kummerfeld, Sisi Ma, Eric Rawls, Bryan Andrews:
Causal Analysis Workshop Series, CAWS 2023, Virtual Event, August 14, 2023. Proceedings of Machine Learning Research 223, PMLR 2023 [contents] - [i8]Joseph D. Ramsey, Bryan Andrews:
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search. CoRR abs/2308.07346 (2023) - [i7]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 abs/2310.17679 (2023) - [i6]Eric Rawls, Bryan Andrews, Kelvin Lim, Erich Kummerfeld:
Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study. CoRR abs/2312.12678 (2023) - 2022
- [c4]Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey:
Greedy relaxations of the sparsest permutation algorithm. UAI 2022: 1052-1062 - [i5]Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey:
Greedy Relaxations of the Sparsest Permutation Algorithm. CoRR abs/2206.05421 (2022) - [i4]Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes:
The m-connecting imset and factorization for ADMG models. CoRR abs/2207.08963 (2022) - 2020
- [c3]Bryan Andrews:
On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge. AISTATS 2020: 4002-4011 - [c2]Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu:
Learning Latent Causal Structures with a Redundant Input Neural Network. CD@KDD 2020: 62-91 - [i3]Jonathan D. Young, Bryan Andrews, Gregory F. Cooper, Xinghua Lu:
Learning Latent Causal Structures with a Redundant Input Neural Network. CoRR abs/2003.13135 (2020)
2010 – 2019
- 2019
- [c1]Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper:
Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types. CD@KDD 2019: 4-21 - 2018
- [j1]Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper:
Scoring Bayesian networks of mixed variables. Int. J. Data Sci. Anal. 6(1): 3-18 (2018) - [i2]Joseph D. Ramsey, Bryan Andrews:
FASK with Interventional Knowledge Recovers Edges from the Sachs Model. CoRR abs/1805.03108 (2018) - 2017
- [i1]Joseph D. Ramsey, Bryan Andrews:
A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables. CoRR abs/1709.04240 (2017)
Coauthor Index
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last updated on 2024-10-21 21:29 CEST by the dblp team
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