Since the start of the War on Poverty in the 1960s, social scientists have developed and refined experimental and quasi-experimental methods for evaluating and understanding the ways in which public policies, programs, and interventions affect people’s lives. The overarching mission of many social scientists is to understand “what works” in education and social policy. These are causal questions about whether an intervention, practice, program, or policy affects some outcome of interest. Although causal questions are not the only relevant questions in program evaluation, they are assumed by many in the fields of public health, economics, social policy, and now education to be the scientific foundation for evidence-based decision making. Fortunately, over the last half-century, two methodological advances have improved the rigor of social science approaches for making causal inferences. The first was acknowledging the primacy of research designs over statistical adjustment procedures. Donald Campbell and colleagues showed how research designs could be used to address many plausible threats to validity. The second methodological advancement was the use of potential outcomes to specify exact causal quantities of interest. This allowed researchers to think systematically about research design assumptions and to develop diagnostic measures for assessing when these assumptions are met. This article reviews important statistical methods for estimating the impact of interventions on outcomes in education settings, particularly programs that are implemented in field, rather than laboratory, settings.