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Double Machine Learning

Double/Debiased Machine Learning (DML), introduced by Chernozhukov et al. (2018), is a semiparametric framework for estimating causal or structural parameters in the presence of high-dimensional controls. It uses flexible machine learning methods to model nuisance functions—the conditional expectations of the outcome and the treatment given covariates—and then constructs a debiased estimator of the target parameter that achieves root-n consistency and valid inference despite the regularization bias inherent in high-dimensional settings.

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Double/Debiased Machine Learning (DML)

Rekod kaedah taksonomik · ml-model / causal-inference

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68. · DOI 10.1111/ectj.12097

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Used in the same domainDoubly Robust Estimationmachine-suggested · Relational suggestion, not evidence.Used in the same domainHeterogeneous Treatment Effectsmachine-suggested · Relational suggestion, not evidence.Same method familyRandom Forestmachine-suggested · Relational suggestion, not evidence.