\section{Conclusion}\label{sec: conclusion}
We proposed two algorithms \SRM\ and \CRM\ that take as input causal graphs, pull observational/interventional arms, and minimize simple and cumulative regret respectively. While \SRM\ works over SMCGs and can handle unobserved variables, \CRM\ works in the fully observable setting. We theoretically and empirically show that our algorithms are better than standard \MAB\ algorithms that do not take causal side-information into account. Further, we show that \SRM\ is almost optimal for causal graphs having an $n$-ary tree structure. In the fully observable setting, our algorithms do not put any restrictions on the graph structure and subsume previous results which imposed strong structural restrictions. We plan to explore  cumulative regret minimization in the presence of UCs in a future work. Another interesting direction is to identify graphs where better simple regret guarantee than \SRM\ can be attained. Finally, obtaining regret guarantees when interventions are non-atomic will be a nice extension to our work.