Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler, Sven Klaassen
arXiv 17 Mar 2021 · Statistics — Machine Learning · publishedJournal of Statistical Software (2024) · 36 citations (OpenAlex)
arXiv:2103.09603 · PDF · DOI · OpenAlex · Extracted main text
The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.
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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, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W… (2018) Double/Debiased Machine Learning for Treatment and Structural Parameters | 1.000 | 20 | 7 | 100% |
| 2 | Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Au Q,… (2019) mlr3: A Modern Object-Oriented Machine Learning Framework in R | 0.737 | 3 | 2 | 100% |
| 3 | Lang M, Au Q, Coors S, Schratz P (2023) mlr3learners: Recommended Learners for mlr3 | 0.737 | 3 | 2 | 100% |
| 4 | Chernozhukov V, Hansen C, Spindler M (2015) Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments | 0.644 | 2 | 2 | 100% |
| 5 | Bach P, Chernozhukov V, Kurz MS, Spindler M (2022) DoubleML – An Object-Oriented Implementation of Double Machine Learning in Python | 0.644 | 2 | 2 | 100% |
| 6 | Chang W (2021) R6: Encapsulated Classes with Reference Semantics | 0.644 | 2 | 2 | 100% |
| 7 | Bach P, Chernozhukov V, Spindler M (2018) Valid Simultaneous Inference in High-Dimensional Settings (with the hdm Package for R) | 0.644 | 2 | 2 | 100% |
| 8 | Belloni A, Chernozhukov V, Fern'andez-Val I, Hansen C (2017) Program Evaluation and Causal Inference with High-Dimensional Data | 0.644 | 2 | 2 | 100% |
| 9 | Dowle M, Srinivasan A (2023) data.table: Extension of data.frame | 0.644 | 2 | 2 | 100% |
| 10 | Friedman J, Hastie T, Tibshirani R (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent | 0.644 | 2 | 2 | 100% |
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