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 sometimes be bounded by front-door and front-door difference-in-differences estimators under relaxed assumptions. We illustrate these points with an application to the National JTPA (Job Training Partnership Act) Study and with an application to Florida’s early in-person voting program. For the JTPA study, we show that an experimental benchmark can be bracketed with front-door and front-door difference-in-differences estimates. Surprisingly, neither of these estimates use control units. For the Florida program, we find some evidence that early in-person voting had small positive effects on turnout in 2008 and 2012. This provides a counterpoint to recent claims that early voting had a negative effect on turnout in 2008.