  In this paper we discuss recent developments in econometrics that we view as
important for empirical researchers working on policy evaluation questions. We
focus on three main areas, where in each case we highlight recommendations for
applied work. First, we discuss new research on identification strategies in
program evaluation, with particular focus on synthetic control methods,
regression discontinuity, external validity, and the causal interpretation of
regression methods. Second, we discuss various forms of supplementary analyses
to make the identification strategies more credible. These include placebo
analyses as well as sensitivity and robustness analyses. Third, we discuss
recent advances in machine learning methods for causal effects. These advances
include methods to adjust for differences between treated and control units in
high-dimensional settings, and methods for identifying and estimating
heterogeneous treatment effects.
