Eric V. Strobl

According to our database, Eric V. Strobl authored at least 21 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Why do probabilistic clinical models fail to transport between sites.

Thomas A. Lasko, Eric V. Strobl, William W. Stead

npj Digit. Medicine, 2024

Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence.

Thomas A. Lasko, John M. Still, Thomas Z. Li, Marco Barbero Mota, William W. Stead, Eric V. Strobl, Bennett A. Landman, Fabien Maldonado

CoRR, 2024

2023

Causal discovery with a mixture of DAGs.

Eric V. Strobl

Mach. Learn., November, 2023

Identifying patient-specific root causes with the heteroscedastic noise model.

Eric V. Strobl, Thomas A. Lasko

J. Comput. Sci., September, 2023

Why Do Clinical Probabilistic Models Fail To Transport Between Sites?

Thomas A. Lasko, Eric V. Strobl, William W. Stead

CoRR, 2023

Counterfactual Formulation of Patient-Specific Root Causes of Disease.

Eric V. Strobl

CoRR, 2023

Sample-Specific Root Causal Inference with Latent Variables.

Eric V. Strobl, Thomas A. Lasko

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Generalizing Clinical Trials with Convex Hulls.

Eric V. Strobl, Thomas A. Lasko

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Root Causal Inference from Single Cell RNA Sequencing with the Negative Binomial.

Eric V. Strobl

Proceedings of the 14th ACM International Conference on Bioinformatics, 2023

2022

Identifying patient-specific root causes of disease.

Eric V. Strobl, Thomas A. Lasko

Proceedings of the BCB '22: 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Northbrook, Illinois, USA, August 7, 2022

2021

Automated hyperparameter selection for the PC algorithm.

Eric V. Strobl

Pattern Recognit. Lett., 2021

Dirac Delta Regression: Conditional Density Estimation with Clinical Trials.

Eric V. Strobl, Shyam Visweswaran

Proceedings of the KDD 2021 Workshop on Causal Discovery, 2021

Synthesized difference in differences.

Eric V. Strobl, Thomas A. Lasko

Proceedings of the BCB '21: 12th ACM International Conference on Bioinformatics, 2021

2019

Estimating and Controlling the False Discovery Rate of the PC Algorithm Using Edge-specific P-Values.

Eric V. Strobl, Peter L. Spirtes, Shyam Visweswaran

ACM Trans. Intell. Syst. Technol., 2019

A constraint-based algorithm for causal discovery with cycles, latent variables and selection bias.

Eric V. Strobl

Int. J. Data Sci. Anal., 2019

Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs.

Eric V. Strobl

Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, 2019

Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures.

Eric V. Strobl, Shyam Visweswaran

Proceedings of the Cause Effect Pairs in Machine Learning, 2019

2018

Fast causal inference with non-random missingness by test-wise deletion.

Eric V. Strobl, Shyam Visweswaran, Peter L. Spirtes

Int. J. Data Sci. Anal., 2018

2014

Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data.

Eric V. Strobl, Shyam Visweswaran

CoRR, 2014

Markov Blanket Ranking using Kernel-based Conditional Dependence Measures.

Eric V. Strobl, Shyam Visweswaran

CoRR, 2014

2013

Deep Multiple Kernel Learning.

Eric V. Strobl, Shyam Visweswaran

Proceedings of the 12th International Conference on Machine Learning and Applications, 2013