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 …

Perhaps no other topic in risk analysis is more difficult, more controversial, or more important
to risk management policy analysts and decision-makers than how to draw valid, correctly …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

One of the central elements of any causal inference is an object called structural causal
model (SCM), which represents a collection of mechanisms and exogenous sources of …

Understanding the laws that govern a phenomenon is the core of scientific progress. This is
especially true when the goal is to model the interplay between different aspects in a causal …

Causal learning is a key challenge in scientific artificial intelligence as it allows researchers
to go beyond purely correlative or predictive analyses towards learning underlying cause …

We establish conditions under which latent causal graphs are nonparametrically identifiable
and can be reconstructed from unknown interventions in the latent space. Our primary focus …

Causal discovery is at the core of human cognition. It enables us to reason about the
environment and make counterfactual predictions about unseen scenarios that can vastly …

One fundamental problem in the empirical sciences is of reconstructing the causal structure
that underlies a phenomenon of interest through observation and experimentation. While …

We study the problem of identifying the best action in a sequential decision-making setting
when the reward distributions of the arms exhibit a non-trivial dependence structure, which …