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Abstract: This paper introduces a novel approach to causal inference utilizing Bayesian bandits and observational data. Traditional methods for establishing causality often necessitate strong assumptions and frequently depend on data collected through interventions, a scenario often impractical or impossible to realize. Our work addresses this limitation by proposing a Bayesian bandit framework that provides a robust solution for causal inference, particularly when interventional data is unavailable. The framework incorporates prior beliefs about causal relationships while dynamically learning from observational data. The core of the method lies in treating each potential causal effect as a bandit problem, allowing for adaptive exploration and exploitation. We demonstrate the potential of this approach through a theoretical analysis and outline key components for implementation. The primary contribution is a practical and flexible method for causal inference that doesn't rely on controlled experiments, enhancing applicability across a wide range of domains.