31 citations · 60 across the 3 of their papers we have counts for
6 papers
Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
The use of genetic variants as instrumental variables - an approach known as Mendelian randomization - is a popular epidemiological method for estimating the causal effect of an ex…
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal in…
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur +1
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence. These values are designed to attribute the difference between a m…
Large-Scale Local Causal Inference of Gene Regulatory Relationships
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that…
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen +1
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that…
A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values
Ruifei Cui, Ioan Gabriel Bucur, Perry Groot +1
We consider the problem of learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values. We propose a novel Bayesian Gaussian copula f…