What question did this study set out to answer?

To develop a general framework for valid statistical inference on low-dimensional treatment and structural parameters in the presence of high-dimensional nuisance parameters estimated by machine learning.

June 24, 2017Econometrics JournalOpen Access

Double/debiased machine learning for treatment and structural parameters

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Authors

VCVictor ChernozhukovRutgers, The State University of New Jersey DCDenis ChetverikovHiroshima University MDMert DemirerMassachusetts Institute of Technology

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Implication

Methodological study demonstrates root-N consistent parameter estimation in high-dimensional models, indicating valid causal inference with machine learning.

Key Points

To develop a general framework for valid statistical inference on low-dimensional treatment and structural parameters in the presence of high-dimensional nuisance parameters estimated by machine learning.
Formulated the Double/Debiased Machine Learning (DML) framework integrating Neyman-orthogonal score equations with cross-fitting (sample-splitting).
Characterized asymptotic theoretical guarantees across partially linear regression, partially linear instrumental variables, and treatment effect models under unconfoundedness, supplemented by three empirical demonstrations.
Demonstrated that DML removes regularization bias and overfitting, yielding point estimators that concentrate in an N^-1/2 neighborhood of the true parameter.
Established that DML estimators are approximately unbiased and normally distributed, enabling valid confidence statements and hypothesis testing using diverse algorithms such as lasso, random forests, and deep neural networks.

Cite This Study

Chernozhukov et al. (2017) studied this question.

synapsesocial.com/papers/69d7fdae3eff0c9dfaae3008 https://doi.org/10.1111/ectj.12097

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Implication

Methodological study demonstrates root-N consistent parameter estimation in high-dimensional models, indicating valid causal inference with machine learning.

Double/debiased machine learning for treatment and structural parameters

Discussion

Member takes

Implication

Key Points

Cite This Study

Double/debiased machine learning for treatment and structural parameters

Discussion

Member takes

Implication

Key Points

Cite This Study

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