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 t...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Wiley
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/138817 |
| _version_ | 1855543728994779136 |
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| author | Bellemare, Marc F. Bloem, Jeffrey R. Wexler, Noah |
| author_browse | Bellemare, Marc F. Bloem, Jeffrey R. Wexler, Noah |
| author_facet | Bellemare, Marc F. Bloem, Jeffrey R. Wexler, Noah |
| author_sort | Bellemare, Marc F. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace138817 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1388172025-10-26T12:55:29Z The paper of how: Estimating treatment effects using the front-door criterion Bellemare, Marc F. Bloem, Jeffrey R. Wexler, Noah economics data approximation transport 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. 2024-08 2024-02-01T17:44:48Z 2024-02-01T17:44:48Z Journal Article https://hdl.handle.net/10568/138817 en Open Access Wiley Bellemare, Marc F.; Bloem, Jeffrey R.; and Wexler, Noah. 2024. The paper of how: Estimating treatment effects using the front-door criterion. Oxford Bulletin of Economics and Statistics 86(4): 951-993. https://doi.org/10.1111/obes.12598 |
| spellingShingle | economics data approximation transport Bellemare, Marc F. Bloem, Jeffrey R. Wexler, Noah The paper of how: Estimating treatment effects using the front-door criterion |
| title | The paper of how: Estimating treatment effects using the front-door criterion |
| title_full | The paper of how: Estimating treatment effects using the front-door criterion |
| title_fullStr | The paper of how: Estimating treatment effects using the front-door criterion |
| title_full_unstemmed | The paper of how: Estimating treatment effects using the front-door criterion |
| title_short | The paper of how: Estimating treatment effects using the front-door criterion |
| title_sort | paper of how estimating treatment effects using the front door criterion |
| topic | economics data approximation transport |
| url | https://hdl.handle.net/10568/138817 |
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