Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing
Author:
Affiliation:
1. Shanghai University of Finance and Economics
2. London School of Economics and Political Science
3. DiDi Chuxing
4. University of North Carolina at Chapel Hill
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2314316
Reference51 articles.
1. Abadie A. J. Angrist and G. Imbens (2002). Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings. Econometrica 70 (1) 91–117.
2. Andriyana Y. and I. Gijbels (2017). Quantile regression in heteroscedastic varying coefficient models. AStA Advances in Statistical Analysis 101 (2) 151–176.
3. Blanco G. X. Chen C. A. Flores and A. Flores-Lagunes (2020). Bounds on average and quantile treatment effects on duration outcomes under censoring selection and noncompliance. Journal of Business & Economic Statistics 38 (4) 901–920.
4. Bondell H. D. B. J. Reich and H. Wang (2010). Noncrossing quantile regression curve estimation. Biometrika 97 (4) 825–838.
5. Chamandy N. (2016). Experimentation in a ridesharing marketplace. URL: https://eng. lyft. com/experimentation-in-a-ridesharing-marketplace-b39db027a66e .
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