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Found 5 papers, 1 papers with code
Efficient Bias Correction for Cross-section and Panel Data
In particular, we find that for a variety of estimators the straightforward bootstrap bias correction gives the same higher-order variance as more complicated analytical or jackknife bias corrections.
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
We also propose a Random Forest method which learns a locally linear representation of the Riesz function.
A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees
Debiased machine learning is a meta algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i. e. scalar summaries, of machine learning algorithms.
Heterogeneous Coefficients, Control Variables, and Identification of Multiple Treatment Effects
Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments.
Minimax Semiparametric Learning With Approximate Sparsity
This paper is about the feasibility and means of root-n consistently estimating linear, mean-square continuous functionals of a high dimensional, approximately sparse regression.
