Understanding causal relationships lies at the heart of scientific inquiry across diverse disciplines including economics, political science, public health, medicine, and education. The potential outcomes framework, originally introduced by Neyman and further developed by Rubin, offers a rigorous conceptual foundation for defining, identifying, and estimating causal effects. In this framework, causal inference is framed as a problem of missing data—at most one potential outcome is observed for each unit, corresponding to the treatment actually received, yet causal effects are contrasts of potential outcomes on a common set of units. Translating this conceptual framework into practice entails numerous methodological and practical challenges. These challenges differ in randomized experiments versus observational studies, but both settings demand careful attention to study design, assumptions, and methods for uncertainty quantification. I explore key issues in causal inference with an emphasis on Bayesian approaches, which offer distinct advantages in both the design and analysis stages of causal studies.