In this review, we discuss approaches for learning causal structure from data, also called
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …

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 …

Bayesian structure learning allows inferring Bayesian network structure from data while
reasoning about the epistemic uncertainty---a key element towards enabling active causal …

Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …

Designers increasingly rely on parametric design studies to explore and improve structural
concepts based on quantifiable metrics, generally either by generating design variations …

Sequential experimental design to discover interventions that achieve a desired outcome is
a key problem in various domains including science, engineering and public policy. When …

Causal discovery from observational and interventional data is challenging due to limited
data and non-identifiability which introduces uncertainties in estimating the underlying …

Causal discovery and causal reasoning are classically treated as separate and consecutive
tasks: one first infers the causal graph, and then uses it to estimate causal effects of …

We introduce a gradient-based approach for the problem of Bayesian optimal experimental
design to learn causal models in a batch setting—a critical component for causal discovery …

Discovering causal structures from data is a challenging inference problem of fundamental
importance in all areas of science. The appealing properties of neural networks have …