Counterfactual time-series prediction with encoder-decoder networks

An important problem in the social sciences is estimating the effect of a policy intervention on an outcome over time. When interventions take place at an aggregate level (e.g., city or state), researchers make causal inferences by comparing the post-intervention outcomes for affected units ("treated") against the outcomes of a group of unaffected units ("control"). The synthetic control method (SCM) (Abadie, Diamond, and Hainmueller 2010) has become a popular method for making causal inferences on observational time-series. The method compares a single treated unit outcome with a synthetic control that combines the outcomes of multiple control units on the basis of their pre-intervention similarity with the treated unit. The SCM has several limitations.