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 …
Learning Mixtures of Unknown Causal Interventions
The ability to conduct interventions plays a pivotal role in learning causal relationships
among variables, thus facilitating applications across diverse scientific disciplines such as …
among variables, thus facilitating applications across diverse scientific disciplines such as …
Linear Causal Bandits: Unknown Graph and Soft Interventions
Designing causal bandit algorithms depends on two central categories of assumptions:(i)
the extent of information about the underlying causal graphs and (ii) the extent of information …
the extent of information about the underlying causal graphs and (ii) the extent of information …
Improved Bound for Robust Causal Bandits with Linear Models
This paper investigates the robustness of causal bandits (CBs) in the face of temporal model
fluctuations. This setting deviates from the existing literature's widely-adopted assumption of …
fluctuations. This setting deviates from the existing literature's widely-adopted assumption of …