Yu-Chin Hsu, Martin Huber, Ying-Ying Lee, Chu-An Liu
arXiv 8 Jun 2021 · Econometrics · publishedThe Review of Economics and Statistics (2024) · 2 citations (OpenAlex)
arXiv:2106.04237 · PDF · DOI · OpenAlex · Extracted main text
While most treatment evaluations focus on binary interventions, a growing literature also considers continuously distributed treatments. We propose a Cram\'{e}r-von Mises-type test for testing whether the mean potential outcome given a specific treatment has a weakly monotonic relationship with the treatment dose under a weak unconfoundedness assumption. In a nonseparable structural model, applying our method amounts to testing monotonicity of the average structural function in the continuous treatment of interest. To flexibly control for a possibly high-dimensional set of covariates in our testing approach, we propose a double debiased machine learning estimator that accounts for covariates in a data-driven way. We show that the proposed test controls asymptotic size and is consistent against any fixed alternative. These theoretical findings are supported by the Monte-Carlo simulations. As an empirical illustration, we apply our test to the Job Corps study and reject a weakly negative relationship between the treatment (hours in academic and vocational training) and labor market performance among relatively low treatment values.
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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 | Colangelo, K. and Y.-Y. Lee (2022) Double debiased machine learning nonparametric inference with continuous treatments | 1.000 | 13 | 3 | 100% |
| 2 | Hsu, Y.-C. and S. Shen (2020) Testing monotonicity of conditional treatment effects under regression discontinuity designs self | 1.000 | 5 | 3 | 100% |
| 3 | Hsu, Y.-C., C.-A. Liu, and X. Shi (2019) Testing generalized regression monotonicity self | 0.950 | 7 | 4 | 86% |
| 4 | Flores, C. A., A. Flores-Lagunes, A. Gonzalez, and T. C. Neumann (2012) Estimating the effects of length of exposure to instruction in a training program: The case of job corps | 0.874 | 7 | 2 | 100% |
| 5 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 5 | 2 | 100% |
| 6 | Huber, M., Y.-C. Hsu, Y.-Y. Lee, and L. Lettry (2020) Direct and indirect effects of continuous treatments based on generalized propensity score weighting self | 0.874 | 5 | 2 | 100% |
| 7 | Lee, Y.-Y (2018) Partial mean processes with generated regressors: Continuous treatment effects and nonseparable models self | 0.874 | 5 | 2 | 100% |
| 8 | Hirano, K. and G. W. Imbens (2004) The propensity score with continuous treatments | 0.843 | 4 | 3 | 75% |
| 9 | Farrell, M. H., T. Liang, and S. Misra (2021) Deep neural networks for estimation and inference | 0.644 | 4 | 1 | 100% |
| 10 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) Locally robust semiparametric estimation | 0.644 | 3 | 2 | 67% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Lee Bounds with a Continuous Treatment in Sample Selection | 0.737 | 3 | 3 |
| 2 | 2604.06643 | 0.737 | 3 | 2 |
| 3 | Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments | 0.405 | 1 | 1 |
| 4 | 2306.05593 | 0.405 | 1 | 1 |