Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

Whereas probabilistic models describe the dependence structure between observed
variables, causal models go one step further: They predict, for example, how cognitive …

The gold standard for discovering causal relations is by means of experimentation. Over the
last decades, alternative methods have been proposed that can infer causal relations …

Causal discovery aims to recover causal structures generating the observational data.
Despite its success in certain problems, in many real-world scenarios the observed …

Many psychological phenomena can be understood as arising from systems of causally
connected components that evolve over time within an individual. In current empirical …

Data fusion deals with combining information from multiple sensors to support decision
making. In such settings, machine learning methods, that principally only take correlation …

Cytometry by time‐of‐flight (CyTOF) has emerged as a high‐throughput single cell
technology able to provide large samples of protein readouts. Already, there exists a large …

We consider the problem of learning a causal graph in the presence of measurement error.
This setting is for example common in genomics, where gene expression is corrupted …

Structure learning algorithms that learn the graph of a Bayesian network from observational
data often do so by assuming the data correctly reect the true distribution of the variables …

Data fusion is the process of integrating data from multiple sources to produce more
accurate and reliable information. It is often the case that data are subject to latent …