Introduces the key conceptual and methodological tools used in public program evaluation, with an emphasis on understanding the forces that shape health and disease as well as various policy solutions. Introduces the Potential Outcomes Framework, also known as the Rubin Causal Model. Establishes the distinction between causation from correlation using counterfactual thinking. Explores a wide variety of experimental and quasi-experimental research methods used to estimate causal effects, including randomized experiments, regression, matching, instrumental variables, fixed effects, regression discontinuity, difference-in-differences, and synthetic control. Many of the causal inference methods that we discuss require statistical and computational training in order to implement. Focuses on the nontechnical conceptual, theoretical, and intuitive underpinnings of these methods that are most salient to policymakers.