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 …

Causal disentanglement seeks a representation of data involving latent variables that are
related via a causal model. A representation is identifiable if both the latent model and the …

We propose an adversarial training procedure for learning a causal implicit generative
model for a given causal graph. We show that adversarial training can be used to learn a …

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 …

We propose a new attribution method for neural networks developed using first principles of
causality (to the best of our knowledge, the first such). The neural network architecture is …

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 Smart Cities use different Internet of Things (IoT) data sources and rely on big data
analytics to obtain information or extract actionable knowledge crucial for urban planners for …

The study of causal inference has seen recent momentum in machine learning and artificial
intelligence (AI), particularly in the domains of transfer learning, reinforcement learning …

We study how to learn optimal interventions sequentially given causal information
represented as a causal graph along with associated conditional distributions. Causal …

We consider the problem of learning causal networks with interventions, when each
intervention is limited in size under Pearl's Structural Equation Model with independent …