Bühlmann Lab

Research focus

ERC Advanced Grant · 2017

Statistics, Prediction and Causality for Large-Scale Data

Understanding cause-effect relationships between variables is of great interest in many fields of science. However, causal inference from data is much more ambitious and difficult than inferring (undirected) measures of association such as correlations, partial correlations or multivariate regression coefficients, mainly because of fundamental identifiability problems. A main objective of the proposal is to exploit advantages from large-scale heterogeneous data for causal inference where heterogeneity arises from different experimental conditions or different unknown sub-populations. A key idea is to consider invariance or stability across different experimental conditions of certain…

From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.