A0950
Title: The basis for inference based on synthetic control methods
Authors: David Hirshberg - Emory University (United States) [presenting]
Abstract: Synthetic Control methods are becoming popular far beyond the context of comparative case studies in which they were first proposed. It is no longer the rule that they are used only when we have one (or few) treated units. But despite recent attention, there is little consensus on when they work and how to do inference based on them. That there is no one way to think about panel data makes this difficult. In some interpretations, we are solving what is essentially a matrix completion problem with noise that is completely unrelated to the selection of treatment; in others, we are inverse propensity weighting to adjust for the selection of treatment based on past outcomes, noise and all. We will discuss some results characterizing synthetic control estimation based on these two interpretations, drawing on synthetic control estimators for panel data as well as that on covariate balancing or calibrated inverse propensity weighting estimators for cross-sectional data. We will highlight some issues that become apparent when we try to mix these perspectives, approaching inference based on the selection of treatment from a perspective in which behaviors specific to individual units, i.e. fixed effects---interactive or otherwise, are needed to explain the heterogeneity of the data.
