Joseph D. Ramsey

Affiliations:
  • Carnegie Mellon University, Department of Philosophy, Pittsburgh, PA, USA


According to our database1, Joseph D. Ramsey authored at least 41 papers between 2002 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search.
CoRR, 2023

Causal-learn: Causal Discovery in Python.
CoRR, 2023

Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Greedy relaxations of the sparsest permutation algorithm.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021
Improving Accuracy of Permutation DAG Search using Best Order Score Search.
CoRR, 2021

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders.
CoRR, 2021

Affect, Support and Personal Factors: Multimodal Causal Models of One-on-one Coaching.
Proceedings of the 14th International Conference on Educational Data Mining, 2021

2020
Causal Discovery from Heterogeneous/Nonstationary Data.
J. Mach. Learn. Res., 2020

2019
Identification of Effective Connectivity Subregions.
CoRR, 2019

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data.
CoRR, 2019

Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis.
Bioinform., 2019

Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types.
Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, 2019

2018
Comparison of strategies for scalable causal discovery of latent variable models from mixed data.
Int. J. Data Sci. Anal., 2018

Scoring Bayesian networks of mixed variables.
Int. J. Data Sci. Anal., 2018

FASK with Interventional Knowledge Recovers Edges from the Sachs Model.
CoRR, 2018

Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

2017
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., 2017

A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables.
CoRR, 2017

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions.
CoRR, 2017

Mixed Graphical Models for Causal Analysis of Multi-modal Variables.
CoRR, 2017

Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

2016
Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value.
CoRR, 2016

Measurement Error and Causal Discovery.
Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application co-located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016

Causal Clustering for 1-Factor Measurement Models.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015
Scaling up Greedy Equivalence Search for Continuous Variables.
CoRR, 2015

Effects of Nonparanormal Transform on PC and GES Search Accuracies.
CoRR, 2015

2014
Non-Gaussian methods and high-pass filters in the estimation of effective connections.
NeuroImage, 2014

Bayesian networks for fMRI: A primer.
NeuroImage, 2014

A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data.
CoRR, 2014

2013
Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism.
Brain Connect., 2013

2011
Network modelling methods for FMRI.
NeuroImage, 2011

On meta-analyses of imaging data and the mixture of records.
NeuroImage, 2011

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study.
NeuroImage, 2011

2010
Actual causation: a stone soup essay.
Synth., 2010

Six problems for causal inference from fMRI.
NeuroImage, 2010

2008
Tabu Search-Enhanced Graphical Models for Classification in High Dimensions.
INFORMS J. Comput., 2008

Discovering Cyclic Causal Models by Independent Components Analysis.
Proceedings of the UAI 2008, 2008

Causal discovery of linear acyclic models with arbitrary distributions.
Proceedings of the UAI 2008, 2008

2006
Adjacency-Faithfulness and Conservative Causal Inference.
Proceedings of the UAI '06, 2006

2002
Classification and filtering of spectra: A case study in mineralogy.
Intell. Data Anal., 2002

Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition.
Data Min. Knowl. Discov., 2002


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