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. We begin by describing the causal inference challenge for evaluating program effects. Then four research designs are discussed that may be used for estimating program impacts. The article highlights what the Campbell tradition identifies as the strongest causal research designs: the randomized experiment and the regression-discontinuity designs. These approaches have the advantage of transparent assumptions for yielding causal effects. The article then discusses weaker but more commonly used approaches estimating effects, including the interrupted time series and the non-equivalent comparison group designs. For the interrupted time series design, differences-in-differences are discussed as a more generalized approach to time series methods; for non-equivalent comparison group designs, the article highlights propensity score matching as a method for creating statistically equivalent groups on the basis of observed covariates. For each research design, references are included that discuss the underlying theory and logic of the method, exemplars of the approach in field settings, and recent methodological extensions to the design. The article concludes with a discussion of practical considerations for evaluating interventions in field settings, including the external validity of estimated effects from impact studies. [This paper was published in "Oxford Bibliographies" 2020.]