This paper considers recent methodological developments in the treatment effects literature, describes their value for applied evaluation work, and suggests next steps. It pays particular attention to documenting the presence of treatment effect heterogeneity, to the quest to attach treatment effect heterogeneity to particular subgroups and other moderators, and to the recent application of machine learning methods in this domain.
Keywords: essential heterogeneity; program evaluation; treatment effects.