CORDIS Project
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This project aims to develop advanced Bayesian nonparametric methods for high-dimensional causal inference, addressing the challenges of variable selection and uncertainty quantification. It seeks to enhance the ability to draw causal conclusions from complex data, with applications in fields such as medicine and econo…
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 incl…
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Netherlands, Amsterdam
Type: Research institute
Activity type: Research Organisations
SME: No
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