Yu-Chin Hsu, Martin Huber, Yu-Min Yen
arXiv 3 Jul 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2307.01049 · PDF · DOI · OpenAlex · Extracted main text
We suggest double/debiased machine learning estimators of direct and indirect quantile treatment effects under a selection-on-observables assumption. This permits disentangling the causal effect of a binary treatment at a specific outcome rank into an indirect component that operates through an intermediate variable called mediator and an (unmediated) direct impact. The proposed method is based on the efficient score functions of the cumulative distribution functions of potential outcomes, which are robust to certain misspecifications of the nuisance parameters, i.e., the outcome, treatment, and mediator models. We estimate these nuisance parameters by machine learning and use cross-fitting to reduce overfitting bias in the estimation of direct and indirect quantile treatment effects. We establish uniform consistency and asymptotic normality of our effect estimators. We also propose a multiplier bootstrap for statistical inference and show the validity of the multiplier bootstrap. Finally, we investigate the finite sample performance of our method in a simulation study and apply it to empirical data from the National Job Corp Study to assess the direct and indirect earnings effects of training.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Farbmacher, H., M. Huber, L. Lafférs, H. Langen, and M. Spindler (2022) Causal mediation analysis with double machine learning | 0.941 | 6 | 4 | 83% |
| 2 | Tchetgen Tchetgen, E. J. and I. Shpitser (2012) Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness and sensitivity analysis | 0.811 | 4 | 2 | 100% |
| 3 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.763 | 9 | 3 | 44% |
| 4 | Zhou, X (2022) Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators | 0.737 | 3 | 2 | 100% |
| 5 | Imai, K., L. Keele, and T. Yamamoto (2010) Identification, Inference and Sensitivity Analysis for Causal Mediation Effects | 0.644 | 2 | 2 | 100% |
| 6 | Chernozhukov, V., I. Fernández-Val, and B. Melly (2013) Inference on Counterfactual Distributions | 0.585 | 3 | 1 | 100% |
| 7 | Vaart, A. W. v. d (1998) Asymptotic Statistics | 0.511 | 3 | 2 | 33% |
| 8 | Bind, M.-A. C., T. J. VanderWeele, J. D. Schwartz, and B. Coull (2017) Quantile causal mediation analysis allowing longitudinal data | 0.511 | 2 | 1 | 100% |
| 9 | Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) Program Evaluation and Causal Inference With High-Dimensional Data | 0.469 | 29 | 4 | 10% |
| 10 | Abadie, A., J. Angrist, and G. Imbens (2002) Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 43 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2603.04109 | 0.405 | 1 | 1 |