Copertina

An Introduction to Double/Debiased Machine Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ahrens, Achim, Chernozhukov, Victor, Hansen, Christian, Kozbur, Damian, Schaffer, Mark, Wiemann, Thomas
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Econometrics Methodology Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Model Averaging and Double Machine Learning
di: Ahrens, Achim, et al.
Pubblicazione: (2024)

ddml: Double/debiased machine learning in Stata
di: Ahrens, Achim, et al.
Pubblicazione: (2023)

Model Averaging and Double Machine Learning
di: Achim Ahrens, et al.
Pubblicazione: (2025)

Applied Causal Inference Powered by ML and AI
di: Chernozhukov, Victor, et al.
Pubblicazione: (2024)

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

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

Long Story Short: Omitted Variable Bias in Causal Machine Learning
di: Chernozhukov, Victor, et al.
Pubblicazione: (2021)

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

Automatic Doubly Robust Forests
di: Chen, Zhaomeng, et al.
Pubblicazione: (2024)

Debiased Ill-Posed Regression
di: Ghassami, AmirEmad, et al.
Pubblicazione: (2025)

Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
di: Fuhr, Jonathan, et al.
Pubblicazione: (2024)

Direct Debiased Machine Learning via Bregman Divergence Minimization
di: Kato, Masahiro
Pubblicazione: (2025)

Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
di: Fuhr, Jonathan, et al.
Pubblicazione: (2024)

Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
di: Baiardi, Anna, et al.
Pubblicazione: (2026)

xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
di: Polselli, Annalivia
Pubblicazione: (2025)

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

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

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

Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects
di: Kato, Masahiro
Pubblicazione: (2024)

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

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

Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
di: More, Sushant, et al.
Pubblicazione: (2024)

Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
di: Bach, Philipp, et al.
Pubblicazione: (2024)

A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regression
di: Kato, Masahiro
Pubblicazione: (2025)

DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
di: Bach, Philipp, et al.
Pubblicazione: (2021)

Identification and Debiased Learning of Causal Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2025)

Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2026)

A Double Machine Learning Approach to Combining Experimental and Observational Data
di: Parikh, Harsh, et al.
Pubblicazione: (2023)

Uniform Inference on High-dimensional Spatial Panel Networks
di: Chernozhukov, Victor, et al.
Pubblicazione: (2021)

Policy Learning with Confidence
di: Chernozhukov, Victor, et al.
Pubblicazione: (2025)

A Gentle Introduction to Conformal Time Series Forecasting
di: Stocker, M., et al.
Pubblicazione: (2025)

Machine-Learning-Assisted Comparison of Regression Functions
di: Yan, Jian, et al.
Pubblicazione: (2025)

Double Robust Bayesian Inference on Average Treatment Effects
di: Breunig, Christoph, et al.
Pubblicazione: (2022)

DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
di: Klaassen, Sven, et al.
Pubblicazione: (2024)

Optimal Categorical Instrumental Variables
di: Wiemann, Thomas
Pubblicazione: (2023)

Debiased Nonparametric Regression for Statistical Inference and Distributionally Robustness
di: Kato, Masahiro
Pubblicazione: (2024)

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

Bayesian Semi-supervised Inference via a Debiased Modeling Approach
di: Sert, Gözde, et al.
Pubblicazione: (2025)

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
di: Athey, Susan, et al.
Pubblicazione: (2023)

Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments
di: Chernozhukov, Victor, et al.
Pubblicazione: (2024)