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 …

In Bayesian structure learning, we are interested in inferring a distribution over the directed
acyclic graph (DAG) structure of Bayesian networks, from data. Defining such a distribution …

Causal inference is essential for data-driven decision making across domains such as
business engagement, medical treatment and policy making. However, research on causal …

Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …

A structural equation model (SEM) is an effective framework to reason over causal
relationships represented via a directed acyclic graph (DAG). Recent advances have …

Abstract Generative Flow Networks (GFlowNets), a class of generative models over discrete
and structured sample spaces, have been previously applied to the problem of inferring the …

Learning structures between groups of variables from data with missing values is an
important task in the real world, yet difficult to solve. One typical scenario is discovering the …

Causal discovery algorithms aim at untangling complex causal relationships from data.
Here, we study causal discovery and inference methods based on staged tree models …

The practical utility of causality in decision-making is widely recognized, with causal
discovery and inference being inherently intertwined. Nevertheless, a notable gap exists in …

Causal structure discovery (CSD) models are making inroads into several domains,
including Earth system sciences. Their widespread adaptation is however hampered by the …