Model-based causal Bayesian optimization
How should we intervene on an unknown structural equation model to maximize a
downstream variable of interest? This setting, also known as causal Bayesian optimization …
downstream variable of interest? This setting, also known as causal Bayesian optimization …
Causal Bandits with General Causal Models and Interventions
This paper considers causal bandits (CBs) for the sequential design of interventions in a
causal system. The objective is to optimize a reward function via minimizing a measure of …
causal system. The objective is to optimize a reward function via minimizing a measure of …
Adversarial Causal Bayesian Optimization
In Causal Bayesian Optimization (CBO), an agent intervenes on a structural causal model
with known graph but unknown mechanisms to maximize a downstream reward variable. In …
with known graph but unknown mechanisms to maximize a downstream reward variable. In …
Combinatorial causal bandits without graph skeleton
In combinatorial causal bandits (CCB), the learning agent chooses a subset of variables in
each round to intervene and collects feedback from the observed variables to minimize …
each round to intervene and collects feedback from the observed variables to minimize …
Robust causal bandits for linear models
The sequential design of experiments for optimizing a reward function in causal systems can
be effectively modeled by the sequential design of interventions in causal bandits (CBs). In …
be effectively modeled by the sequential design of interventions in causal bandits (CBs). In …
Learning good interventions in causal graphs via covering
We study the causal bandit problem that entails identifying a near-optimal intervention from
a specified set A of (possibly non-atomic) interventions over a given causal graph. Here, an …
a specified set A of (possibly non-atomic) interventions over a given causal graph. Here, an …
Asymmetric Graph Error Control with Low Complexity in Causal Bandits
In this paper, the causal bandit problem is investigated, in which the objective is to select an
optimal sequence of interventions on nodes in a causal graph. It is assumed that the graph …
optimal sequence of interventions on nodes in a causal graph. It is assumed that the graph …
Robust causal bandits for linear models
Sequential design of experiments for optimizing a reward function in causal systems can be
effectively modeled by the sequential design of interventions in causal bandits (CBs). In the …
effectively modeled by the sequential design of interventions in causal bandits (CBs). In the …
Graph Identification and Upper Confidence Evaluation for Causal Bandits with Linear Models
In this paper, the causal bandit problem is investigated, in which the objective is to select an
optimal sequence of interventions on nodes in a graph. By exploiting the causal …
optimal sequence of interventions on nodes in a graph. By exploiting the causal …
Causal Contextual Bandits with Adaptive Context
We study a variant of causal contextual bandits where the context is chosen based on an
initial intervention chosen by the learner. At the beginning of each round, the learner selects …
initial intervention chosen by the learner. At the beginning of each round, the learner selects …