Presenter: Adam Glynn
Abstract: In this paper, we develop front-door difference-in-differences estimators that utilize information from post-treatment variables in addition to information from pre-treatment covariates. Even when the front-door criterion does not hold, these estimators allow the identification of causal effects by utilizing assumptions that are analogous to standard difference-in-differences assumptions. We also demonstrate that causal effects can be bounded by front-door and front-door difference-in-differences estimators under relaxed assumptions. We illustrate these points with an application to the effects of early in-person voting on turnout. Despite recent claims that early voting had a negative effect on turnout in 2008, we find evidence that early in-person voting had small positive effects on turnout in Florida in 2008 and 2012.