A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables. - Joseph D. Ramsey; Bryan Andrews (CoRR, 2017)

FASK with Interventional Knowledge Recovers Edges from the Sachs Model. - Joseph D. Ramsey; Bryan Andrews (CoRR, 2018)

Scoring Bayesian networks of mixed variables. - Bryan Andrews; Joseph D. Ramsey; Gregory F. Cooper (Int. J. Data Sci. Anal., 2018)

Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types. - Bryan Andrews; Joseph D. Ramsey; Gregory F. Cooper (CD@KDD, 2019)

On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge. - Bryan Andrews (AISTATS, 2020)

Learning Latent Causal Structures with a Redundant Input Neural Network. - Jonathan D. Young; Bryan Andrews; Gregory F. Cooper; Xinghua Lu 001 (CD@KDD, 2020)

Greedy relaxations of the sparsest permutation algorithm. - Wai-Yin Lam; Bryan Andrews; Joseph D. Ramsey (UAI, 2022)

The m-connecting imset and factorization for ADMG models. - Bryan Andrews; Gregory F. Cooper; Thomas S. Richardson 001; Peter Spirtes (CoRR, 2022)

Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search. - Joseph D. Ramsey; Bryan Andrews (CAWS, 2023)

Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees. - Bryan Andrews; Joseph D. Ramsey; Ruben Sanchez-Romero; Jazmin Camchong; Erich Kummerfeld (NeurIPS, 2023)

Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study. - Eric Rawls; Bryan Andrews; Kelvin Lim; Erich Kummerfeld (CoRR, 2023)

A new method for estimating the probability of causal relationships from observational data: Application to the study of the short-term effects of air pollution on cardiovascular and respiratory disease. - Bryan Andrews; Chirayu Wongchokprasitti; Shyam Visweswaran; Chirag M. Lakhani; Chirag J. Patel; Gregory F. Cooper (Artif. Intell. Medicine, 2023)

Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method. - Bryan Andrews; Erich Kummerfeld (CoRR, 2024)

Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth. - Joseph D. Ramsey; Bryan Andrews; Peter Spirtes (CoRR, 2024)

An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts. - Ritwick Banerjee; Bryan Andrews; Erich Kummerfeld (CoRR, 2025)