Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler
arXiv 7 Apr 2021 · Statistics — Machine Learning · publishedJournal of Statistical Software (2024) · 36 citations (OpenAlex)
arXiv:2104.03220 · PDF · DOI · OpenAlex · Extracted main text
DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. It contains functionalities for valid statistical inference on causal parameters when the estimation of nuisance parameters is based on machine learning methods. The object-oriented implementation of DoubleML provides a high flexibility in terms of model specifications and makes it easily extendable. The package is distributed under the MIT license and relies on core libraries from the scientific Python ecosystem: scikit-learn, numpy, pandas, scipy, statsmodels and joblib. Source code, documentation and an extensive user guide can be found at https://github.com/DoubleML/doubleml-for-py and https://docs.doubleml.org.
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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 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 5 | 5 | 100% |
| 2 | F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. G… (2011) Scikit-learn: Machine learning in Python | 0.843 | 3 | 3 | 100% |
| 3 | N.-C. Chang (2020) Double/debiased machine learning for difference-in-differences models | 0.644 | 2 | 2 | 100% |
| 4 | L. Mackey, V. Syrgkanis, and I. Zadik (2018) Orthogonal machine learning: Power and limitations | 0.644 | 2 | 2 | 100% |
| 5 | P. Bach, V. Chernozhukov, M. S. Kurz, and M. Spindler (2021) DoubleML – An object-oriented implementation of double machine learning in R, 2021 self | 0.405 | 1 | 1 | 100% |
| 6 | H. Chen, T. Harinen, J.-Y. Lee, M. Yung, and Z. Zhao (2002) CausalML: Python package for causal machine learning, 2020 | 0.405 | 1 | 1 | 100% |
| 7 | H. D. Chiang, K. Kato, Y. Ma, and Y. Sasaki (2021) Multiway cluster robust double/debiased machine learning | 0.405 | 1 | 1 | 100% |
| 8 | K. Battocchi, E. Dillon, M. Hei, G. Lewis, P. Oka, M. Oprescu, and V… (2021) EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation | 0.405 | 1 | 1 | 100% |
| 9 | N. Kallus and M. Uehara (2020) Double reinforcement learning for efficient off-policy evaluation in markov decision processes | 0.405 | 1 | 1 | 100% |
| 10 | M. S. Kurz (2021) Distributed double machine learning with a serverless architecture self | 0.405 | 1 | 1 | 100% |
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