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An Introduction to Double/Debiased Machine Learning

Achim Ahrens, Victor Chernozhukov, Christian Hansen, Damian Kozbur, Mark Schaffer, Thomas Wiemann

arXiv 11 Apr 2025 · Econometrics · 3 citations (OpenAlex)

arXiv:2504.08324 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper provides a practical introduction to Double/Debiased Machine Learning (DML). DML provides a general approach to performing inference about a target parameter in the presence of nuisance parameters. The aim of DML is to reduce the impact of nuisance parameter estimation on estimators of the parameter of interest. We describe DML and its two essential components: Neyman orthogonality and cross-fitting. We highlight that DML reduces functional form dependence and accommodates the use of complex data types, such as text data. We illustrate its application through three empirical examples that demonstrate DML's applicability in cross-sectional and panel settings.

Citation extraction

144
references
227
in-text mentions
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distinct cited
22
self-citations
19,397
main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Dube, Arindrajit, Jacobs, Jeff, Naidu, Suresh, Suri, Siddharth (2020) Monopsony in Online Labor Markets1.000123100%
2Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2018) Double/debiased machine learning for treatment and structural parameters self0.97413792%
3Ahrens, Achim, Hansen, Christian B., Schaffer, Mark E., Wiemann, Tho… (2025) Model averaging and double machine learning self0.92843100%
4Sun, Liyang, Abraham, Sarah (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects0.87472100%
5Dobkin, Carlos, Finkelstein, Amy, Kluender, Raymond, Notowidigdo, Ma… (2018) The economic consequences of hospital admissions0.87462100%
6Callaway, Brantly, Sant'Anna, Pedro H. C (2021) Difference-in-differences with multiple time periods0.87452100%
7Chernozhukov, Victor, Escanciano, Juan Carlos, Ichimura, Hidehiko, N… (2022) Locally robust semiparametric estimation self0.8434475%
8Newey, Whitney K (1994) The asymptotic variance of semiparametric estimators0.8434475%
9Kennedy, Edward H (2023) Semiparametric doubly robust targeted double machine learning: a review0.7373367%
10Angrist, Joshua D., Imbens, Guido W., Krueger, Alan B (1999) Jackknife Instrumental Variables Estimation0.73732100%

Showing the top 10 of 144 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Better Understanding Triple Differences Estimators0.64422
2Automatic Locally Robust GMM with Machine-Learning-Generated Regressors0.51122
3Bayesian Double Machine Learning for Causal Inference0.51121
4Sequential Decision Problems with Missing Feedback0.40511
5Double Machine Learning for Time Series0.40511
6Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments0.40511