  We study the problem of learning 'good' interventions in a stochastic
environment modeled by its underlying causal graph. Good interventions refer to
interventions that maximize rewards. Specifically, we consider the setting of a
pre-specified budget constraint, where interventions can have non-uniform
costs. We show that this problem can be formulated as maximizing the expected
reward for a stochastic multi-armed bandit with side information. We propose an
algorithm to minimize the cumulative regret in general causal graphs. This
algorithm trades off observations and interventions based on their costs to
achieve the optimal reward. This algorithm generalizes the state-of-the-art
methods by allowing non-uniform costs and hidden confounders in the causal
graph. Furthermore, we develop an algorithm to minimize the simple regret in
the budgeted setting with non-uniform costs and also general causal graphs. We
provide theoretical guarantees, including both upper and lower bounds, as well
as empirical evaluations of our algorithms. Our empirical results showcase that
our algorithms outperform the state of the art.
