7 citations · 10 across the 4 of their papers we have counts for
4 papers
An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts
Ritwick Banerjee, Bryan Andrews, Erich Kummerfeld
Despite the accelerating presence of exploratory causal analysis in modern science and medicine, the available non-experimental methods for validating causal models are not well ch…
Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method
Bryan Andrews, Erich Kummerfeld
The number of artificial intelligence algorithms for learning causal models from data is growing rapidly. Most ``causal discovery'' or ``causal structure learning'' algorithms are…
Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study
Eric Rawls, Bryan Andrews, Kelvin Lim +1
Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this pape…
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Bryan Andrews, Joseph Ramsey, Ruben Sanchez-Romero +2
Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of l…