Data-driven exploration of heterogeneous gasoline price elasticities using generalized random forests
Author:
Publisher
Springer Science and Business Media LLC
Subject
Transportation,Development,Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11116-023-10417-w.pdf
Reference70 articles.
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2. Alberini, A., Di Cosmo, V., Horvath, M.: Time on the road and the price of gasoline: evidence from ATUS and NHTS. Transp. Res. Part D Transp. Environ. (2022). https://doi.org/10.1016/j.trd.2022.103334
3. Athey, S., Imbens, G.: Recursive partitioning for heterogeneous causal effects. Proc. Natl. Acad. Sci. (2016). https://doi.org/10.1073/pnas.1510489113
4. Athey, S., Imbens, G.W.: Machine learning methods that economists should know about. Ann. Rev. Econ. (2019). https://doi.org/10.1146/annurev-economics-080217-053433
5. Athey, S., Wager, S.: Estimating treatment effects with causal forests: an application. Observ. Stud. (2019). https://doi.org/10.1353/obs.2019.0001
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