While population-scale neuroimaging studies offer the promise of discovery and
characterisation of subtle risk factors, massive sample sizes increase the power for both …

Administrative data are becoming increasingly important. They are typically the side effect of
some operational exercise and are often seen as having significant advantages over …

Purpose of review Although deep learning represents an exciting platform for the
development of risk stratification models, it is challenging to evaluate these models beyond …

Abstract “What-if” questions are intuitively generated and commonly asked during the design
process. Engineers and architects need to inherently conduct design decisions, progressing …

Interpretable machine learning (IML) is concerned with the behavior and the properties of
machine learning models. Scientists, however, are only interested in models as a gateway to …

We present Causal Generative Neural Networks (CGNNs) to learn functional causal models
from observational data. CGNNs leverage conditional independencies and distributional …

Causal discovery and causal effect estimation are two fundamental tasks in causal
inference. While many methods have been developed for each task individually, statistical …

Aims: Electronic health records (EHR), containing rich clinical histories of large patient
populations, can provide evidence for clinical decisions when evidence from trials and …

In this guide, we present how to perform constraint-based causal discovery using three
popular software packages: pcalg (with add-ons tpc and micd), bnlearn, and TETRAD. We …

Causal discovery algorithms aim to identify causal relations from observational data and
have become a popular tool for analysing genetic regulatory systems. In this work, we …