The Paper of How: Estimating Treatment Effects Using the Front‐Door Criterion

We illustrate the use of Pearl's (1995) front‐door criterion with observational data with an application in which the assumptions for point identification hold. For identification, the front‐door criterion leverages exogenous mediator variables on the causal path. After a preliminary discussion of the identification assumptions behind and the estimation framework used for the front‐door criterion, we present an empirical application. In our application, we look at the effect of deciding to share an Uber or Lyft ride on tipping by exploiting the algorithm‐driven exogenous variation in whether one actually shares a ride conditional on authorizing sharing, the full fare paid, and origin–destination fixed effects interacted with two‐hour interval fixed effects. We find that most of the observed negative relationship between choosing to share a ride and tipping is driven by customer selection into sharing rather than by sharing itself. In the Appendix, we explore the consequences of violating the identification assumptions for the front‐door criterion.