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Orthogonal/Double Machine Learning — econml 0.15.1 documentation

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Double Machine Learning is a method for estimating (heterogeneous) treatment effects when all potential confounders/controls (factors that simultaneously had a direct effect on the treatment decision in the collected data and the observed outcome) are observed, but are either too many (high-dimensional) for classical statistical approaches to be applicable or their effect on the treatment and outcome cannot be satisfactorily modeled by parametric functions (non-parametric). Both of these latter problems can be addressed via machine learning techniques (see e.g. [Chernozhukov2016]).

Orthogonal/Double Machine Learning - econml 0.16.0 documentation EconML User Guide Estimation Methods under Unconfoundedness Orthogonal/Double Machine Learning View page source Orthogonal/Double Machine Learning  What is it?  Double Machine Learning is a method for estimating (heterogeneous) treatment effects when all potential confounders/controls (factors that simultaneously had a direct effect on the treatment decision in the collected data and the observed outcome) are observed, but are either too many (high-dimensional) for classical statistical approaches to be applicable or their effe

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