Collaborative Research Team Project #19
Improving Robust High-Dimensional Causal Inference and Prediction Modelling
This project explores methods to improve high-dimensional causal inference and prediction modelling in biomedical sciences.
Research Category: Health & Biology
Why Study Complex Data in Precision Medicine?
Recent advances in “-omics” technologies are allowing scientists to simultaneously collect large amounts of clinical data. This is revolutionizing the way we can measure pathogenic processes or responses to therapies.
This high-dimensional data has the potential to improve disease prevention and diagnosis. However, it presents significant challenges due to measurement errors, outliers, multivariate responses, and complex correlation structures.
Advancements in this area are essential for building useful models in precision medicine.
Areas of Exploration
Advanced Regularized Regression
Includes reducing variance and decreasing sample errors in biometric data.
Regularized Instrumental Variables
Includes correcting for measurement errors in biometric data.
Matrix-Valued Causal Models
Includes the development of causal models in high-dimensional settings.
Solving Global Challenges
Research Team’s Goal
To develop an advanced analytical framework for managing complex data in biomedical sciences.
People Behind the Project
Gabriela Cohen-Freue | University of British Columbia
Celia Greenwood | Lady Davis Institute for Medical Research, McGill University
Sahir Bhatnagar | McGill University
Tom Blydt-Hansen | University of British Columbia
Dehan Kong | University of Toronto
Karim Oualkacha | Université du Québec à Montréal
Brent Richards | McGill University
David Soave | Laurier University
Linbo Wang | University of Toronto
Zhaolei Zhang | University of Toronto
Find Related Programs
Improving Robust High-Dimensional Causal Inference and Prediction Modelling is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.
CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.