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.