3 citations · 3 across the 4 of their papers we have counts for
4 papers
Transportability without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets
Konstantina Lelova, Gregory F. Cooper, Sofia Triantafillou
Transporting causal information across populations is a critical challenge in clinical decision-making. Causal modeling provides criteria for identifiability and transportability,…
The m-connecting imset and factorization for ADMG models
Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson +1
Directed acyclic graph (DAG) models have become widely studied and applied in statistics and machine learning -- indeed, their simplicity facilitates efficient procedures for learn…
Causal Markov Boundaries
Sofia Triantafillou, Fattaneh Jabbari, Greg Cooper
Feature selection is an important problem in machine learning, which aims to select variables that lead to an optimal predictive model. In this paper, we focus on feature selection…
Learning Adjustment Sets from Observational and Limited Experimental Data
Sofia Triantafillou, Gregory Cooper
Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their in…