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

Marc F. Bellemare, Jeffrey R Bloem, Noah Wexler

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalOxford Bulletin of Economics and Statistics
DOIs
StateAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd.

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