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 …

A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …

As a pivotal component to attaining generalizable solutions in human intelligence,
reasoning provides great potential for reinforcement learning (RL) agents' generalization …

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 …

In causal bandit problems the action set consists of interventions on variables of a causal
graph. Several researchers have recently studied such bandit problems and pointed out …

Learning good interventions in a causal graph can be modelled as a stochastic multi-armed
bandit problem with side-information. First, we study this problem when interventions are …

A fundamental difficulty of causal learning is that causal models can generally not be fully
identified based on observational data only. Interventional data, that is, data originating from …

Learning causal relationships between variables is a fundamental task in causal inference
and directed acyclic graphs (DAGs) are a popular choice to represent the causal …

A growing body of work has begun to study intervention design for efficient structure learning
of causal directed acyclic graphs (DAGs). A typical setting is a\emph {causally sufficient} …

We consider the problem of experimental design for learning causal graphs that have a tree
structure. We propose an adaptive framework that determines the next intervention based on …