arXiv 30 Jan 2022 · Econometrics · 2 citations (OpenAlex)
arXiv:2201.12692 · PDF · DOI · OpenAlex · Extracted main text
Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sample-splitting and cross-fitting to reduce the overfitting bias. In both synthetic and semi-synthetic simulations we find that the performance of the meta-learners in finite samples greatly depends on the estimation procedure. The results imply that sample-splitting and cross-fitting are beneficial in large samples for bias reduction and efficiency of the meta-learners, respectively, whereas full-sample estimation is preferable in small samples. Furthermore, we derive practical recommendations for application of specific meta-learners in empirical studies depending on particular data characteristics such as treatment shares and sample size.
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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 | Sekhon, Jasjeet S., Bickel, Peter J., Yu, Bin (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 1.000 | 37 | 6 | 100% |
| 2 | Knaus, Michael C, Lechner, Michael, Strittmatter, Anthony (2021) Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence | 1.000 | 18 | 5 | 100% |
| 3 | Nie, X, Wager, S (2021) Quasi-oracle estimation of heterogeneous treatment effects | 1.000 | 18 | 4 | 100% |
| 4 | Kennedy, Edward H (2020) Optimal doubly robust estimation of heterogeneous causal effects | 1.000 | 16 | 4 | 100% |
| 5 | Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 14 | 3 | 100% |
| 6 | Lechner, Michael (2018) Modified Causal Forests for Estimating Heterogeneous Causal Effects | 1.000 | 11 | 5 | 100% |
| 7 | Newey, Whitney K., Robins, James R (2018) Cross-fitting and fast remainder rates for semiparametric estimation | 1.000 | 11 | 4 | 100% |
| 8 | Jacob, Daniel (2020) Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects | 1.000 | 10 | 4 | 100% |
| 9 | Wager, Stefan, Athey, Susan (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 1.000 | 8 | 3 | 100% |
| 10 | Athey, Susan, Tibshirani, Julie, Wager, Stefan (2019) Generalized random forests | 1.000 | 7 | 6 | 100% |
Showing the top 10 of 120 scored citations.