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 …

In supervised learning, acquiring labeled training data for a predictive model can be very
costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is …

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

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 …

Abstract We present Causal Amortized Active Structure Learning (CAASL), an active
intervention design policy that can select interventions that are adaptive, real-time and that …

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 introduce the problem of active causal structure learning with advice. In the typical well-
studied setting, the learning algorithm is given the essential graph for the observational …

We study two problems related to recovering causal graphs from interventional data:(i)
$\textit {verification} $, where the task is to check if a purported causal graph is correct, and …