arXiv 4 Nov 2024 · Econometrics
arXiv:2411.01864 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the properties of debiased machine learning (DML) estimators under a novel asymptotic framework, offering insights for improving the performance of these estimators in applications. DML is an estimation method suited to economic models where the parameter of interest depends on unknown nuisance functions that must be estimated. It requires weaker conditions than previous methods while still ensuring standard asymptotic properties. Existing theoretical results do not distinguish between two alternative versions of DML estimators, DML1 and DML2. Under a new asymptotic framework, this paper demonstrates that DML2 asymptotically dominates DML1 in terms of bias and mean squared error, formalizing a previous conjecture based on simulation results regarding their relative performance. Additionally, this paper provides guidance for improving the performance of DML2 in applications.
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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 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 12 | 6 | 100% |
| 2 | Sant’Anna, P. H. and J. Zhao (2020) Doubly robust difference-in-differences estimators | 1.000 | 8 | 3 | 100% |
| 3 | Linton, O (1995) Second order approximation in the partially linear regression model | 1.000 | 6 | 4 | 100% |
| 4 | Newey, W. K. and R. J. Smith (2004) Higher order properties of GMM and generalized empirical likelihood estimators | 1.000 | 6 | 3 | 100% |
| 5 | Andrews, D. W (1994) Asymptotics for semiparametric econometric models via stochastic equicontinuity | 1.000 | 5 | 3 | 100% |
| 6 | Hirano, K., G. W. Imbens, and G. Ridder (2003) Efficient estimation of average treatment effects using the estimated propensity score | 1.000 | 5 | 3 | 100% |
| 7 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) a): Locally robust semiparametric estimation | 0.928 | 4 | 3 | 100% |
| 8 | Newey, W. K (1994) The asymptotic variance of semiparametric estimators | 0.928 | 4 | 3 | 100% |
| 9 | Ahrens, A., C. B. Hansen, M. E. Schaffer, and T. Wiemann (2024) a): ddml: Double/debiased machine learning in Stata | 0.843 | 3 | 3 | 100% |
| 10 | Ahrens, A., C. B. Hansen, M. E. Schaffer, and T. Wiemann (2024) b): Model averaging and double machine learning | 0.843 | 3 | 3 | 100% |
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