The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion - Marc F. Bellemare, Jeffrey R. Bloem, and Noah Wexler.
Abstract: We illustrate the use of Pearl's (1995) front-door criterion with an application in which the assumptions for point identification hold with observational data. 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.
Biography: Marc F. Bellemare is McKnight Presidential Chair in Applied Economics, Distinguished McKnight University Professor, Distinguished University Teaching Professor, and Northrop Professor at the University of Minnesota, where his research focuses on agricultural economics and applied econometrics. He is a Fellow of the Agricultural and Applied Economics Association, and his work has been featured in The Economist, The Guardian, the New York Times, National Public Radio, the Wall Street Journal, and the Washington Post.