| dc.contributor | 國貿系 | |
| dc.creator (作者) | 顏佑銘 | |
| dc.creator (作者) | Hsu, Yu-Chin;Huber, Martin;Yen, Yu-Min | |
| dc.date (日期) | 2026-05 | |
| dc.date.accessioned | 12-May-2026 10:01:09 (UTC+8) | - |
| dc.date.available | 12-May-2026 10:01:09 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-May-2026 10:01:09 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=182462 | - |
| dc.description.abstract (摘要) | We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of potential outcomes. We demonstrate uniform consistency and asymptotic normality of our effect estimators under specific regularity conditions and propose a multiplier bootstrap for statistical inference. Finally, we apply our method to data from the National Job Corps Study to assess the direct effect of training on earnings and the indirect effect operating through work experience. | |
| dc.format.extent | 109 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Journal of Bussiness & Economic Statistics, pp.1-31 | |
| dc.subject (關鍵詞) | Causal inference; Efficient influence function; Mediation analysis; Semiparametric efficiency | |
| dc.title (題名) | Estimation of Direct and Indirect Quantile Treatment Effects with Double Machine Learning | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1080/07350015.2026.2654889 | |
| dc.doi.uri (DOI) | https://doi.org/10.1080/07350015.2026.2654889 | |
