Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …

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 …

Structural causal bandit provides a framework for online decision-making problems when
causal information is available. It models the stochastic environment with a structural causal …

Empowered by neural networks, deep reinforcement learning (DRL) achieves tremendous
empirical success. However, DRL requires a large dataset by interacting with the …

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 …

Abstract Machine learning (ML) models have been widely used to make predictions. Instead
of a predictive statement about future outcomes, in many situations we want to pursue a …

This paper studies the problem of designing an optimal sequence of interventions in a
causal graphical model to minimize cumulative regret with respect to the best intervention in …

We explore algorithms to select actions in the causal bandit setting where the learner can
choose to intervene on a set of random variables related by a causal graph, and the learner …

Abstract We introduce causal Markov Decision Processes (C-MDPs), a new formalism for
sequential decision making which combines the standard MDP formulation with causal …

In online recommendation, customers arrive in a sequential and stochastic manner from an
underlying distribution and the online decision model recommends a chosen item for each …