arXiv 3 Mar 2024 · Econometrics · publishedEconomics Letters (2024) · 3 citations (OpenAlex)
arXiv:2403.01585 · PDF · DOI · OpenAlex · Extracted main text
Machine learning methods, particularly the double machine learning (DML) estimator (Chernozhukov et al., 2018), are increasingly popular for the estimation of the average treatment effect (ATE). However, datasets often exhibit unbalanced treatment assignments where only a few observations are treated, leading to unstable propensity score estimations. We propose a simple extension of the DML estimator which undersamples data for propensity score modeling and calibrates scores to match the original distribution. The paper provides theoretical results showing that the estimator retains the DML estimator's asymptotic properties. A simulation study illustrates the finite sample performance of the estimator.
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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 | 8 | 5 | 100% |
| 2 | Japkowicz, N. and S. Stephen (2002) The class imbalance problem: A systematic study | 0.928 | 4 | 3 | 100% |
| 3 | Hahn, J (1998) On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects | 0.843 | 3 | 3 | 100% |
| 4 | Huber, M., M. Lechner, and C. Wunsch (2013) The performance of estimators based on the propensity score | 0.811 | 4 | 2 | 100% |
| 5 | Athey, S. and G. W. Imbens (2019) Machine Learning Methods That Economists Should Know About | 0.644 | 2 | 2 | 100% |
| 6 | Künzel, S. R., J. S. Sekhon, P. J. Bickel, and B. Yu (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.644 | 2 | 2 | 100% |
| 7 | Wager, S (2022) STATS 361: Causal Inference, Lecture Notes | 0.511 | 3 | 2 | 33% |
| 8 | Knaus, M. C., M. Lechner, and A. Strittmatter (2022) Heterogeneous employment effects of job search programs: A machine learning approach | 0.511 | 2 | 1 | 100% |
| 9 | Pozzolo, A. D., O. Caelen, R. A. Johnson, and G. Bontempi (2015) Calibrating Probability with Undersampling for Unbalanced Classification, in | 0.511 | 2 | 1 | 100% |
| 10 | Bach, P., O. Schacht, V. Chernozhukov, S. Klaassen, and M. Spindler (2024) Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study, Preprint (arXiv:2402.04674) | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Sensitivity Analysis for Treatment Effects in Difference-in-Differences Models using Riesz Representation | 0.405 | 1 | 1 |