Double/Debiased Machine Learning
Chernozhukov et al. orthogonalization for causal estimation with ML nuisance.
Double/Debiased Machine Learning. Chernozhukov et al. orthogonalization for causal estimation with ML nuisance.
Recent technical contributions
A handful of recent papers carry the methodological frontier of double/debiased machine learning forward. Double/debiased machine learning for treatment and structural parameters (Chernozhukov et al., 2018) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for double/debiased machine learning include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- paper · primary · 2018chernozhukov-2018, chetverikov-2018, demirer-2018, duflo-2018, hansen-2018, newey-2018, robins-2018
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