Copertina

Efficient Adaptive Experimental Design for Average Treatment Effect Estimation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kato, Masahiro, Ishihara, Takuya, Honda, Junya, Narita, Yusuke
Natura:	Preprint
Pubblicazione:	2020
Soggetti:	Machine Learning Econometrics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Adaptive Experimental Design for Policy Learning
di: Kato, Masahiro, et al.
Pubblicazione: (2024)

Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
di: Kato, Masahiro, et al.
Pubblicazione: (2024)

Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
di: Kato, Masahiro
Pubblicazione: (2025)

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

Minimax and Bayes Optimal Adaptive Experimental Design for Treatment Choice
di: Kato, Masahiro
Pubblicazione: (2025)

Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learning
di: Kato, Masahiro
Pubblicazione: (2025)

PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
di: Kato, Masahiro, et al.
Pubblicazione: (2025)

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
di: Kato, Masahiro
Pubblicazione: (2025)

A Note on Doubly Robust Estimator in Regression Discontinuity Designs
di: Kato, Masahiro
Pubblicazione: (2024)

Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
di: Nagai, Tomoko, et al.
Pubblicazione: (2024)

Riesz Regression As Direct Density Ratio Estimation
di: Kato, Masahiro
Pubblicazione: (2025)

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

Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression
di: Kato, Masahiro
Pubblicazione: (2025)

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

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

Dynamic Local Average Treatment Effects
di: Sojitra, Ravi B., et al.
Pubblicazione: (2024)

On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization
di: Byambadalai, Undral, et al.
Pubblicazione: (2025)

Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
di: Huang, Yiyan, et al.
Pubblicazione: (2024)

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

Conformal Predictive Portfolio Selection
di: Kato, Masahiro
Pubblicazione: (2024)

Conformal Prediction for Nonparametric Instrumental Regression
di: Kato, Masahiro
Pubblicazione: (2026)

Identification of Average Treatment Effects in Nonparametric Panel Models
di: Athey, Susan, et al.
Pubblicazione: (2025)

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

Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks
di: Hirata, Tomu, et al.
Pubblicazione: (2025)

Asymptotically Unbiased Synthetic Control Methods by Moment Matching
di: Kato, Masahiro, et al.
Pubblicazione: (2023)

Gaussian and Bootstrap Approximation for Matching-based Average Treatment Effect Estimators
di: Shi, Zhaoyang, et al.
Pubblicazione: (2024)

Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
di: Chihara, Naoki, et al.
Pubblicazione: (2026)

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

General Bayesian Policy Learning
di: Kato, Masahiro
Pubblicazione: (2026)

Minimax and Bayes Optimal Best-Arm Identification
di: Kato, Masahiro
Pubblicazione: (2025)

Locally Optimal Fixed-Budget Best Arm Identification in Two-Armed Gaussian Bandits with Unknown Variances
di: Kato, Masahiro
Pubblicazione: (2023)

Causal-Policy Forest for End-to-End Policy Learning
di: Kato, Masahiro
Pubblicazione: (2025)

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

Minimax Optimal Simple Regret in Two-Armed Best-Arm Identification
di: Kato, Masahiro
Pubblicazione: (2024)

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

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

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

Worst-Case Optimal Multi-Armed Gaussian Best Arm Identification with a Fixed Budget
di: Kato, Masahiro
Pubblicazione: (2023)

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

Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification
di: Kato, Masahiro
Pubblicazione: (2024)