Deep Multiple Kernel Learning. - Eric V. Strobl; Shyam Visweswaran (ICMLA (1), 2013)

Markov Blanket Ranking using Kernel-based Conditional Dependence Measures. - Eric V. Strobl; Shyam Visweswaran (CoRR, 2014)

Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data. - Eric V. Strobl; Shyam Visweswaran (CoRR, 2014)

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)

Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures. - Eric V. Strobl; Shyam Visweswaran (Cause Effect Pairs in Machine Learning, 2019)

Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs. - Eric V. Strobl (CD@KDD, 2019)

A constraint-based algorithm for causal discovery with cycles, latent variables and selection bias. - Eric V. Strobl (Int. J. Data Sci. Anal., 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)

Synthesized difference in differences. - Eric V. Strobl; Thomas A. Lasko (BCB, 2021)

Dirac Delta Regression: Conditional Density Estimation with Clinical Trials. - Eric V. Strobl; Shyam Visweswaran (CD@KDD, 2021)

Automated hyperparameter selection for the PC algorithm. - Eric V. Strobl (Pattern Recognit. Lett., 2021)

Identifying patient-specific root causes of disease. - Eric V. Strobl; Thomas A. Lasko (BCB, 2022)

Root Causal Inference from Single Cell RNA Sequencing with the Negative Binomial. - Eric V. Strobl (BCB, 2023)

Generalizing Clinical Trials with Convex Hulls. - Eric V. Strobl; Thomas A. Lasko (CLeaR, 2023)

Sample-Specific Root Causal Inference with Latent Variables. - Eric V. Strobl; Thomas A. Lasko (CLeaR, 2023)

Why Do Clinical Probabilistic Models Fail To Transport Between Sites? - Thomas A. Lasko; Eric V. Strobl; William W. Stead (CoRR, 2023)

Identifying patient-specific root causes with the heteroscedastic noise model. - Eric V. Strobl; Thomas A. Lasko (J. Comput. Sci., 2023)

Causal discovery with a mixture of DAGs. - Eric V. Strobl (Mach. Learn., 2023)

Mitigating pathogenesis for target discovery and disease subtyping. - Eric V. Strobl; Thomas A. Lasko; Eric R. Gamazon (Comput. Biol. 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)

Counterfactual formulation of patient-specific root causes of disease. - Eric V. Strobl (J. Biomed. Informatics, 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; William W. Stead; John M. Still; Thomas Z. Li; Michael N. Kammer; Marco Barbero Mota; Eric V. Strobl; Bennett A. Landman; Fabien Maldonado (J. Biomed. Informatics, 2025)

A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records. - Marco Barbero Mota; John M. Still; Jorge L. Gamboa; Eric V. Strobl; Charles M. Stein; Vivian K. Kawai; Thomas A. Lasko (CoRR, 2025)

Learning Causally Predictable Outcomes from Psychiatric Longitudinal Data. - Eric V. Strobl (CoRR, 2025)