Joseph D. Ramsey

54 papers A* 2A 5B 1Journal 41Unranked 5

Year	Rank	Type	Title / Venue / Authors
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
2025	—	`conf`	Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models. CLeaR Yi Han, Joseph D. Ramsey, Peter Spirtes
2025	J	`jnl`	The causal structure of galactic astrophysics. CoRR Harry Desmond, Joseph D. Ramsey
2024	J	`jnl`	Causal-learn: Causal Discovery in Python. J. Mach. Learn. Res. Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
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`	Causal-learn: Causal Discovery in Python. CoRR Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
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
2021	B	`conf`	Affect, Support and Personal Factors: Multimodal Causal Models of One-on-one Coaching. EDM Lujie Karen Chen, Joseph D. Ramsey, Artur Dubrawski
2021	J	`jnl`	FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. CoRR Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph D. Ramsey, Zhifeng Hao, Clark Glymour
2021	J	`jnl`	Improving Accuracy of Permutation DAG Search using Best Order Score Search. CoRR Joseph D. Ramsey
2020	J	`jnl`	Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
2019	J	`jnl`	Causal Discovery from Heterogeneous/Nonstationary Data. CoRR Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
2019	J	`jnl`	Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. CoRR Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph D. Ramsey, Madelyn Glymour, Clark Glymour
2019	J	`jnl`	Identification of Effective Connectivity Subregions. CoRR Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Clark Glymour
2019	—	`conf`	Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types. CD@KDD Bryan Andrews, Joseph D. Ramsey, Gregory F. Cooper
2019	J	`jnl`	Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. Bioinform. 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
2018	A	`conf`	Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour
2018	J	`jnl`	Comparison of strategies for scalable causal discovery of latent variable models from mixed data. Int. J. Data Sci. Anal. Vineet K. Raghu, Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, Panayiotis V. Benos
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
2017	J	`jnl`	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. Joseph D. Ramsey, Madelyn Glymour, Ruben Sanchez-Romero, Clark Glymour
2017	J	`jnl`	Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. CoRR Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour
2017	—	`conf`	Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints. ECML/PKDD (2) Fattaneh Jabbari, Joseph D. Ramsey, Peter Spirtes, Gregory F. Cooper
2017	J	`jnl`	Mixed Graphical Models for Causal Analysis of Multi-modal Variables. CoRR Andrew J. Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos
2016	A*	`conf`	Causal Clustering for 1-Factor Measurement Models. KDD Erich Kummerfeld, Joseph D. Ramsey
2016	J	`jnl`	Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value. CoRR Joseph D. Ramsey
2016	—	`conf`	Measurement Error and Causal Discovery. CFA@UAI Richard Scheines, Joseph D. Ramsey
2015	J	`jnl`	Effects of Nonparanormal Transform on PC and GES Search Accuracies. CoRR Joseph D. Ramsey
2015	J	`jnl`	Scaling up Greedy Equivalence Search for Continuous Variables. CoRR Joseph D. Ramsey
2014	J	`jnl`	A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data. CoRR Joseph D. Ramsey
2014	J	`jnl`	Bayesian networks for fMRI: A primer. NeuroImage Jeanette A. Mumford, Joseph D. Ramsey
2014	J	`jnl`	Non-Gaussian methods and high-pass filters in the estimation of effective connections. NeuroImage Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour
2013	J	`jnl`	Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism. Brain Connect. Catherine Hanson, Stephen José Hanson, Joseph D. Ramsey, Clark Glymour
2012	J	`jnl`	Adjacency-Faithfulness and Conservative Causal Inference. CoRR Joseph D. Ramsey, Jiji Zhang, Peter Spirtes
2012	J	`jnl`	Causal discovery of linear acyclic models with arbitrary distributions CoRR Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
2012	J	`jnl`	Discovering Cyclic Causal Models by Independent Components Analysis CoRR Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer
2011	J	`jnl`	Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage Joseph D. Ramsey, Stephen José Hanson, Clark Glymour
2011	J	`jnl`	Network modelling methods for FMRI. NeuroImage Stephen M. Smith, Karla L. Miller, Gholamreza Salimi Khorshidi, Matthew A. Webster, Christian F. Beckmann, Thomas E. Nichols, Joseph D. Ramsey, Mark William Woolrich
2011	J	`jnl`	On meta-analyses of imaging data and the mixture of records. NeuroImage Joseph D. Ramsey, Peter Spirtes, Clark Glymour
2010	J	`jnl`	Actual causation: a stone soup essay. Synth. Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang
2010	J	`jnl`	Six problems for causal inference from fMRI. NeuroImage Joseph D. Ramsey, Stephen José Hanson, Catherine Hanson, Yaroslav O. Halchenko, Russell A. Poldrack, Clark Glymour
2008	A	`conf`	Causal discovery of linear acyclic models with arbitrary distributions. UAI Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
2008	A	`conf`	Discovering Cyclic Causal Models by Independent Components Analysis. UAI Gustavo Lacerda, Peter Spirtes, Joseph D. Ramsey, Patrik O. Hoyer
2008	J	`jnl`	Tabu Search-Enhanced Graphical Models for Classification in High Dimensions. INFORMS J. Comput. Xue Bai, Rema Padman, Joseph D. Ramsey, Peter Spirtes
2006	A	`conf`	Adjacency-Faithfulness and Conservative Causal Inference. UAI Joseph D. Ramsey, Jiji Zhang, Peter Spirtes
2002	J	`jnl`	Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition. Data Min. Knowl. Discov. Joseph D. Ramsey, Paul Gazis, Ted Roush, Peter Spirtes, Clark Glymour
2002	J	`jnl`	Classification and filtering of spectra: A case study in mineralogy. Intell. Data Anal. Jonathan Moody, Ricardo Bezerra de Andrade e Silva, Joseph Vanderwaart, Joseph D. Ramsey, Clark Glymour