Achim Ahrens, Christian B. Hansen, Mark E. Schaffer, Thomas Wiemann
arXiv 3 Jan 2024 · Econometrics · publishedJournal of Applied Econometrics (2025) · 17 citations (OpenAlex)
arXiv:2401.01645 · PDF · DOI · OpenAlex · Extracted main text
This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: short-stacking exploits the cross-fitting step of DDML to substantially reduce the computational burden and pooled stacking enforces common stacking weights over cross-fitting folds. Using calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches based on single pre-selected learners. We provide Stata and R software implementing our proposals.
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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, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2018) Double/debiased machine learning for treatment and structural parameters self | 1.000 | 8 | 4 | 100% |
| 2 | Zhu, Ying (2023) Omitted variable bias of Lasso-based inference methods: A finite sample analysis | 0.874 | 6 | 2 | 100% |
| 3 | Ahrens, Achim, Hansen, Christian B., Schaffer, Mark E., Wiemann, Tho… (2024) ddml: Double/debiased machine learning in Stata self | 0.843 | 4 | 3 | 75% |
| 4 | Belloni, Alexandre, Chernozhukov, Victor, Hansen, Christian (2014) Inference on treatment effects after selection among high-dimensional controls self | 0.811 | 4 | 2 | 100% |
| 5 | Belloni, A, Chernozhukov, V, Fernández-Val, I, Hansen, C (2017) Program Evaluation and Causal Inference With High-Dimensional Data self | 0.644 | 4 | 1 | 100% |
| 6 | Card, David, DellaVigna, Stefano, Funk, Patricia, Iriberri, Nagore (2020) Are Referees and Editors in Economics Gender Neutral? | 0.644 | 4 | 1 | 100% |
| 7 | Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome (2009) The Elements of Statistical Learning | 0.644 | 2 | 2 | 100% |
| 8 | Wang, Xiaoqian, Hyndman, Rob J., Li, Feng, Kan, Yanfei (2023) Forecast combinations: An over 50-year review | 0.644 | 2 | 2 | 100% |
| 9 | Ash, Elliott, Hansen, Stephen (2023) Text Algorithms in Economics | 0.644 | 2 | 2 | 100% |
| 10 | Ash, Elliott, Chen, Daniel L., Ornaghi, Arianna (2024) Gender Attitudes in the Judiciary: Evidence from US Circuit Courts | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 82 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | An Introduction to Double/Debiased Machine Learning | 0.928 | 4 | 3 |
| 2 | On the Asymptotic Properties of Debiased Machine Learning Estimators | 0.843 | 3 | 3 |
| 3 | Deep Learning for Individual Heterogeneity | 0.511 | 2 | 2 |
| 4 | Double Machine Learning for Time Series | 0.405 | 1 | 1 |