arXiv 6 Apr 2020 · Econometrics · publishedJournal of Business and Economic Statistics (2025) · 23 citations (OpenAlex)
arXiv:2004.03036 · PDF · DOI · OpenAlex · Extracted main text
We propose a doubly robust inference method for causal effects of continuous treatment variables, under unconfoundedness and with nonparametric or high-dimensional nuisance functions. Our double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial effects are asymptotically normal with non-parametric convergence rates. The first-step estimators for the nuisance conditional expectation function and the conditional density can be nonparametric or ML methods. Utilizing a kernel-based doubly robust moment function and cross-fitting, we give high-level conditions under which the nuisance function estimators do not affect the first-order large sample distribution of the DML estimators. We provide sufficient low-level conditions for kernel, series, and deep neural networks. We justify the use of kernel to localize the continuous treatment at a given value by the Gateaux derivative. We implement various ML methods in Monte Carlo simulations and an empirical application on a job training program evaluation
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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 | Kennedy, E. H., Z. Ma, M. D. McHugh, and D. S. Small (2017) Nonparametric methods for doubly robust estimation of continuous treatment effects | 1.000 | 8 | 3 | 100% |
| 2 | Farrell, M. H., T. Liang, and S. Misra (2021) Deep neural networks for estimation and inference | 0.971 | 12 | 6 | 92% |
| 3 | Lee, Y.-Y (2018) Partial mean processes with generated regressors: Continuous treatment effects and nonseparable models self | 0.928 | 5 | 4 | 80% |
| 4 | Hsu, Y.-C., M. Huber, Y.-Y. Lee, and L. Lettry (2020) Direct and indirect effects of continuous treatments based on generalized propensity score weighting | 0.928 | 5 | 3 | 80% |
| 5 | Chernozhukov, V., W. Newey, and R. Singh (2022) Automatic debiased machine learning of causal and structural effects | 0.843 | 3 | 3 | 100% |
| 6 | Kallus, N. and A. Zhou (2018) Policy evaluation and optimization with continuous treatments | 0.843 | 3 | 3 | 100% |
| 7 | Su, L., T. Ura, and Y. Zhang (2019) Non-separable models with high-dimensional data | 0.822 | 9 | 4 | 56% |
| 8 | 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 | 0.811 | 4 | 2 | 100% |
| 9 | Lee, D. S. (2009, 07) (2009) Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects | 0.811 | 4 | 2 | 100% |
| 10 | Ichimura, H. and W. K. Newey (2022) The influence function of semiparametric estimators | 0.754 | 7 | 4 | 43% |
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