This chapter provides a brief review of several important ideas in causality. We define potential outcomes, a pair of outcomes for each individual denoting their response if they had gotten treatment and their response if they had gotten control. Typically, we only observe one of the potential outcomes. We define some causal estimands, such as the average causal difference, vaccine efficacy, and the Mann–Whitney parameter. We discuss estimation of the average causal difference from a matched experiment and a randomized study. Using a hypothetical vaccine study, we discuss why causal inference requires more care and assumptions for observational studies than for experiments. We work through a study to estimate the average causal effect on compliers from a randomized study with imperfect compliance. We define principled adjustments for randomized studies. We discuss interference in causality. We review causal analysis with propensity scores for observational studies. We define directed acyclic graphs (DAGs) and show how they can be used to define the backdoor criterion and confounders. Finally, we discuss instrumental variables analysis.