Causal Estimands for Policy Evaluation and Beyond

This paper reviews various estimands used in modern scientific and applied research to operationalize causal inquiries within the Rubin Causal Model framework. I first introduce the most widely utilized average treatment effects, such as ATE, ATT, and ATC. I then describe their popular extensions, including those targeting local and conditional treatment effects; causal interactions and mediation; effects for non-continuous outcomes, as well as for multi-valued and continuous treatments; and longitudinal treatment effects. For each of these estimands, a substantive explanation is provided, along with examples of research questions they can address. The key assumptions necessary for the identification of the most widely used effects are also discussed.