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ORCIDJoseph D. Ramsey
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- affiliation: Carnegie Mellon University, Department of Philosophy, Pittsburgh, PA, USA
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2020 – today
- 2024
[j17]Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang:
Causal-learn: Causal Discovery in Python. J. Mach. Learn. Res. 25: 60:1-60:8 (2024)- [i21]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
- [c12]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 - [c11]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 - [i20]Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu
, Peter Spirtes, Kun Zhang:
Causal-learn: Causal Discovery in Python. CoRR abs/2307.16405 (2023) - [i19]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) - [i18]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) - 2022
- [c10]Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey:
Greedy relaxations of the sparsest permutation algorithm. UAI 2022: 1052-1062 - [i17]Wai-Yin Lam, Bryan Andrews, Joseph D. Ramsey:
Greedy Relaxations of the Sparsest Permutation Algorithm. CoRR abs/2206.05421 (2022) - 2021
- [c9]Lujie Karen Chen, Joseph D. Ramsey, Artur Dubrawski:
Affect, Support and Personal Factors: Multimodal Causal Models of One-on-one Coaching. EDM 2021 - [i16]Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph D. Ramsey, Zhifeng Hao, Clark Glymour:
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. CoRR abs/2103.14238 (2021) - [i15]Joseph D. Ramsey:
Improving Accuracy of Permutation DAG Search using Best Order Score Search. CoRR abs/2108.10141 (2021) - 2020
- [j16]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. 21: 89:1-89:53 (2020)
2010 – 2019
- 2019
- [j15]Andrew J. Sedgewick, Kristina Buschur, Ivy Shi, Joseph D. Ramsey, Vineet K. Raghu, Dimitris V. Manatakis, Yingze Zhang, Jessica Bon, Divay Chandra, Chad Karoleski, Frank C. Sciurba, Peter Spirtes, Clark Glymour, Panayiotis V. Benos:
Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. Bioinform. 35(7): 1204-1212 (2019) - [c8]Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper:
Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types. CD@KDD 2019: 4-21 - [i14]Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph D. Ramsey, Madelyn Glymour, Clark Glymour:
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. CoRR abs/1902.10073 (2019) - [i13]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. CoRR abs/1903.01672 (2019) - [i12]Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Clark Glymour:
Identification of Effective Connectivity Subregions. CoRR abs/1908.03264 (2019) - 2018
- [j14]Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper:
Scoring Bayesian networks of mixed variables. Int. J. Data Sci. Anal. 6(1): 3-18 (2018) - [j13]Vineet K. Raghu, Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, Panayiotis V. Benos:
Comparison of strategies for scalable causal discovery of latent variable models from mixed data. Int. J. Data Sci. Anal. 6(1): 33-45 (2018) - [c7]Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI 2018: 1063-1072 - [i11]Joseph D. Ramsey, Bryan Andrews:
FASK with Interventional Knowledge Recovers Edges from the Sachs Model. CoRR abs/1805.03108 (2018) - 2017
- [j12]Joseph D. Ramsey, Madelyn Glymour, Ruben Sanchez-Romero, Clark Glymour:
A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images. Int. J. Data Sci. Anal. 3(2): 121-129 (2017) - [c6]Fattaneh Jabbari, Joseph D. Ramsey, Peter Spirtes, Gregory F. Cooper:
Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints. ECML/PKDD (2) 2017: 142-157 - [i10]Andrew J. Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos:
Mixed Graphical Models for Causal Analysis of Multi-modal Variables. CoRR abs/1704.02621 (2017) - [i9]Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. CoRR abs/1706.03768 (2017) - [i8]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) - 2016
- [c5]Erich Kummerfeld, Joseph D. Ramsey:
Causal Clustering for 1-Factor Measurement Models. KDD 2016: 1655-1664 - [c4]Richard Scheines, Joseph D. Ramsey:
Measurement Error and Causal Discovery. CFA@UAI 2016: 1-7 - [i7]Joseph D. Ramsey:
Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value. CoRR abs/1610.00378 (2016) - 2015
- [i6]Joseph D. Ramsey:
Effects of Nonparanormal Transform on PC and GES Search Accuracies. CoRR abs/1505.01825 (2015) - [i5]Joseph D. Ramsey:
Scaling up Greedy Equivalence Search for Continuous Variables. CoRR abs/1507.07749 (2015) - 2014
- [j11]Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour:
Non-Gaussian methods and high-pass filters in the estimation of effective connections. NeuroImage 84: 986-1006 (2014) - [j10]Jeanette A. Mumford, Joseph D. Ramsey:
Bayesian networks for fMRI: A primer. NeuroImage 86: 573-582 (2014) - [i4]Joseph D. Ramsey:
A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data. CoRR abs/1401.5031 (2014) - 2013
- [j9]Catherine Hanson, Stephen José Hanson, Joseph D. Ramsey, Clark Glymour:
Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism. Brain Connect. 3(6): 578-589 (2013) - 2012
- [i3]Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu:
Causal discovery of linear acyclic models with arbitrary distributions. CoRR abs/1206.3260 (2012) - [i2]Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer:
Discovering Cyclic Causal Models by Independent Components Analysis. CoRR abs/1206.3273 (2012) - [i1]Joseph D. Ramsey, Jiji Zhang, Peter Spirtes:
Adjacency-Faithfulness and Conservative Causal Inference. CoRR abs/1206.6843 (2012) - 2011
- [j8]Stephen M. Smith, Karla L. Miller, Gholamreza Salimi Khorshidi, Matthew A. Webster, Christian F. Beckmann, Thomas E. Nichols, Joseph D. Ramsey, Mark William Woolrich:
Network modelling methods for FMRI. NeuroImage 54(2): 875-891 (2011) - [j7]Joseph D. Ramsey, Peter Spirtes, Clark Glymour:
On meta-analyses of imaging data and the mixture of records. NeuroImage 57(2): 323-330 (2011) - [j6]Joseph D. Ramsey, Stephen José Hanson, Clark Glymour:
Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage 58(3): 838-848 (2011) - 2010
- [j5]Joseph D. Ramsey, Stephen José Hanson, Catherine Hanson, Yaroslav O. Halchenko, Russell A. Poldrack, Clark Glymour:
Six problems for causal inference from fMRI. NeuroImage 49(2): 1545-1558 (2010) - [j4]Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang:
Actual causation: a stone soup essay. Synth. 175(2): 169-192 (2010)
2000 – 2009
- 2008
- [j3]Xue Bai, Rema Padman, Joseph D. Ramsey, Peter Spirtes:
Tabu Search-Enhanced Graphical Models for Classification in High Dimensions. INFORMS J. Comput. 20(3): 423-437 (2008) - [c3]Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu:
Causal discovery of linear acyclic models with arbitrary distributions. UAI 2008: 282-289 - [c2]Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer:
Discovering Cyclic Causal Models by Independent Components Analysis. UAI 2008: 366-374 - 2006
- [c1]Joseph D. Ramsey, Jiji Zhang, Peter Spirtes:
Adjacency-Faithfulness and Conservative Causal Inference. UAI 2006 - 2002
- [j2]Joseph D. Ramsey, Paul Gazis, Ted Roush, Peter Spirtes, Clark Glymour:
Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition. Data Min. Knowl. Discov. 6(3): 277-293 (2002) - [j1]Jonathan Moody, Ricardo Bezerra de Andrade e Silva, Joseph Vanderwaart, Joseph D. Ramsey, Clark Glymour:
Classification and filtering of spectra: A case study in mineralogy. Intell. Data Anal. 6(6): 517-530 (2002)
Coauthor Index
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last updated on 2024-10-21 20:31 CEST by the dblp team
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