Ioan Gabriel Bucur

Research

I am currently working on developing robust methods for causal inference and causal discovery. I want to help policymakers extract meaningful information from data so that they can make better decisions.

Publications

Understanding the Assumptions Underlying Mendelian Randomization

Christiaan de Leeuw, Jeanne Savage, Ioan Gabriel Bucur, Tom Heskes, and Danielle Posthuma

Published in European Journal of Human Genetics

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models

Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, and Tom Claassen

Presented at Conference on Neural Information Processing Systems (NeurIPS 2020)

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models

Ioan Gabriel Bucur, Tom Claassen, and Tom Heskes

Presented at Conference on Uncertainty in Artificial Intelligence (UAI 2020)

Large-Scale Local Causal Inference of Gene Regulatory Relationships

Ioan Gabriel Bucur, Tom Claassen, and Tom Heskes

Published in International Journal of Approximate Reasoning

Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach

Ioan Gabriel Bucur, Tom Claassen, and Tom Heskes

Published in Statistical Methods in Medical Research

A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values

Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, and Tom Heskes

Published in Statistics and Computing

Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness

Ioan Gabriel Bucur, Tom Claassen, and Tom Heskes

Presented at Conference on Artificial Intelligence and Statistics (AISTATS 2017)

Visiting Address

Room M1.02.13 - Faculty of Science
Toernooiveld 212, 6525EC Nijmegen