In the complex world of educational policy, causal inferences will be debated. As we review non-experimental designs in educational policy, we focus on how to clarify and focus the terms of debate. We begin by presenting the potential outcomes/counterfactual framework and then describe approximations to the counterfactual generated from the general linear model, regression discontinuity designs, the propensity to be treated, and instrumental variables. Noting the advantages and limitations of each method, we turn to literature showing that conventional linear models, which can be extended to fixed and random effects models and difference-in-differences designs, are particularly useful when covariates include pre-tests. Recognizing that no technique fully resolves concerns about inferences, we quantify the conditions necessary to invalidate an inference. We review additional considerations including accounting for context, heterogeneous treatment effects, and mediation. Throughout we focus on the role of the researcher in making analytic choices, accentuated in the Quantcrit framework. [This chapter will be published in: "Handbook on Education Policy Research," edited by L. Cohen-Vogel et al., American Educational Research Association.]