Cover Image

On the Asymptotic Properties of Debiased Machine Learning Estimators

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Velez, Amilcar
Format:	Preprint
Published:	2024
Subjects:	Econometrics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Local Projection Residual Bootstrap for AR(1) Models
by: Velez, Amilcar
Published: (2023)

Debiased Machine Learning U-statistics
by: Escanciano, Juan Carlos, et al.
Published: (2022)

Machine Learning Debiasing with Conditional Moment Restrictions: An Application to LATE
by: Argañaraz, Facundo, et al.
Published: (2024)

Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions
by: Park, Gyungbae
Published: (2024)

Automatic Debiased Machine Learning of Structural Parameters with General Conditional Moments
by: Argañaraz, Facundo
Published: (2025)

Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters
by: Semenova, Vira
Published: (2023)

Neighborhood Stability in Double/Debiased Machine Learning with Dependent Data
by: Cao, Jianfei, et al.
Published: (2025)

An Introduction to Double/Debiased Machine Learning
by: Ahrens, Achim, et al.
Published: (2025)

Higher-Order Debiased Estimators for General Treatment Models
by: Zhang, Yulin, et al.
Published: (2026)

Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models
by: Argañaraz, Facundo, et al.
Published: (2025)

Identification and Inference on Treatment Effects under Covariate-Adaptive Randomization and Imperfect Compliance
by: Bugni, Federico A., et al.
Published: (2024)

On the power properties of inference for parameters with interval identified sets
by: Bugni, Federico A., et al.
Published: (2024)

Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence
by: Chen, Kaicheng, et al.
Published: (2026)

On a Debiased and Semiparametric Efficient Changes-in-Changes Estimator
by: Sun, Jinghao, et al.
Published: (2025)

Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration
by: Ballinari, Daniele, et al.
Published: (2024)

(Debiased) Inference for Fixed Effects Estimators with Three-Dimensional Panel and Network Data
by: Czarnowske, Daniel, et al.
Published: (2025)

On the Asymptotics of the Minimax Linear Estimator
by: Kong, Jing
Published: (2025)

Asymptotic Properties of the Distributional Synthetic Controls
by: Zhang, Lu, et al.
Published: (2024)

ScoreMatchingRiesz: Score Matching for Debiased Machine Learning and Policy Path Estimation
by: Kato, Masahiro
Published: (2025)

Double/Debiased Machine Learning for Treatment and Causal Parameters
by: Chernozhukov, Victor, et al.
Published: (2016)

Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning
by: Haddad, Michel F. C., et al.
Published: (2024)

Asymptotic Properties of the Maximum Likelihood Estimator for Markov-switching Observation-driven Models
by: Krabbe, Frederik
Published: (2024)

Root-$n$ Asymptotically Normal Maximum Score Estimation
by: Liu, Nan, et al.
Published: (2026)

Debiased Kernel Estimation of Spot Volatility in the Presence of Infinite Variation Jumps
by: Boniece, B. Cooper, et al.
Published: (2025)

A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting
by: Wu, Zeqi, et al.
Published: (2025)

Direct Debiased Machine Learning via Bregman Divergence Minimization
by: Kato, Masahiro
Published: (2025)

Debiasing and $t$-tests for synthetic control inference on average causal effects
by: Chernozhukov, Victor, et al.
Published: (2018)

Debiased Inference for Dynamic Nonlinear Panels with Multi-dimensional Heterogeneities
by: Leng, Xuan, et al.
Published: (2023)

Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Empirical Likelihood Estimators
by: Lee, Seojeong
Published: (2018)

Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators
by: Lee, Seojeong
Published: (2018)

Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
by: Dalal, Abhinandan, et al.
Published: (2024)

Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
by: Kato, Masahiro, et al.
Published: (2023)

A Note on the Asymptotic Properties of the GLS Estimator in Multivariate Regression with Heteroskedastic and Autocorrelated Errors
by: Moriya, Koichiro, et al.
Published: (2025)

genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
by: Kato, Masahiro
Published: (2026)

Debiased Ill-Posed Regression
by: Ghassami, AmirEmad, et al.
Published: (2025)

Triple/Double-Debiased Lasso
by: Chetverikov, Denis, et al.
Published: (2026)

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
by: Kato, Masahiro
Published: (2026)

A Unified Framework for Debiased Machine Learning: Riesz Representer Fitting under Bregman Divergence
by: Kato, Masahiro
Published: (2026)

Asymptotic Theory for Clustered Samples
by: Hansen, Bruce E., et al.
Published: (2019)

Debiased Regression for Root-N-Consistent Conditional Mean Estimation
by: Kato, Masahiro
Published: (2024)