Pas Lab

Research focus

ERC Starting Grant · 2022

High-dimensional nonparametric Bayesian causal inference

Causal conclusions are at the center of research, yet notoriously difficult to obtain. Many research studies report correlations only, which, in line with the maxim, do not imply causation. With correlations, one can make predictions. With causation, one can intervene. Paradoxically, causal inference can become harder when more data becomes available. In the by now increasingly common high-dimensional settings which are the focus of this proposal, including all variables is impossible while including too few can severely bias results. Variable selection becomes necessary, yet available methods are in short supply. My aim is to develop Bayesian nonparametric methods and theory for…

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