Image de couverture de livre

Perturbed Double Machine Learning: Nonstandard Inference Beyond the Parametric Length

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zheng, Mengchu, Bonvini, Matteo, Guo, Zijian
Format:	Preprint
Publié:	2025
Sujets:	Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
par: Guo, Zijian, et autres
Publié: (2022)

Fast convergence rates for dose-response estimation
par: Bonvini, Matteo, et autres
Publié: (2022)

Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
par: Scheidegger, Cyrill, et autres
Publié: (2025)

Doubly-robust inference and optimality in structure-agnostic models with smoothness
par: Bonvini, Matteo, et autres
Publié: (2024)

On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression
par: Dukes, Oliver, et autres
Publié: (2021)

Semiparametric Inference for Partially Identifiable Data Fusion Estimands via Double Machine Learning
par: Jiang, Yicong, et autres
Publié: (2025)

Semiparametric sensitivity analysis: unmeasured confounding in observational studies
par: Nabi, Razieh, et autres
Publié: (2021)

Forests for Differences: Robust Causal Inference Beyond Parametric DiD
par: Souto, Hugo Gobato, et autres
Publié: (2025)

Multi-source Stable Variable Importance Measure via Adversarial Machine Learning
par: Wang, Zitao, et autres
Publié: (2024)

Statistical Inference in High-dimensional Poisson Regression with Applications to Mediation Analysis
par: Rakshit, Prabrisha, et autres
Publié: (2024)

Propensity Score Propagation: A General Framework for Design-Based Inference with Unknown Propensity Scores
par: Heng, Siyu, et autres
Publié: (2026)

Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond
par: Shao, Daqian, et autres
Publié: (2025)

Randomization-Based Inference for Average Treatment Effects in Inexactly Matched Observational Studies
par: Zhu, Jianan, et autres
Publié: (2023)

Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes
par: Wu, Bohan, et autres
Publié: (2024)

Double Robust Variance Estimation with Parametric Working Models
par: Shook-Sa, Bonnie E., et autres
Publié: (2024)

Perturbation-based Inference for Extreme Value Index
par: Tang, Yiwei, et autres
Publié: (2025)

Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
par: Dalal, Abhinandan, et autres
Publié: (2024)

Identification and Inference with Invalid Instruments
par: Kang, Hyunseung, et autres
Publié: (2024)

Bayesian Non-Parametric Inference for Lévy Measures in State-Space Models
par: Lin, Bill Z., et autres
Publié: (2025)

An Introduction to Double/Debiased Machine Learning
par: Ahrens, Achim, et autres
Publié: (2025)

Robust mean change point testing in high-dimensional data with heavy tails
par: Li, Mengchu, et autres
Publié: (2023)

Federated Transfer Learning with Differential Privacy
par: Li, Mengchu, et autres
Publié: (2024)

Neighborhood Stability in Double/Debiased Machine Learning with Dependent Data
par: Cao, Jianfei, et autres
Publié: (2025)

Double/Debiased Machine Learning for Continuous Treatment Effects in Panel Data with Endogeneity
par: Wu, Peikai, et autres
Publié: (2026)

Hawkes Processes with Variable Length Memory: Existence, Inference and Application to Neuronal Activity
par: Quayle, Sacha, et autres
Publié: (2025)

Beyond Vintage Rotation: Bias-Free Sparse Representation Learning with Oracle Inference
par: Cui, Chengyu, et autres
Publié: (2026)

UD-DML: Uniform Design Subsampling for Double Machine Learning over Massive Data
par: Qu, Yuanke, et autres
Publié: (2026)

Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference
par: van der Laan, Lars, et autres
Publié: (2025)

Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
par: Wang, Zhenyu, et autres
Publié: (2023)

Task-Agnostic Machine-Learning-Assisted Inference
par: Miao, Jiacheng, et autres
Publié: (2024)

Distributionally Robust Synthetic Control: Ensuring Robustness Against Highly Correlated Controls and Weight Shifts
par: Koo, Taehyeon, et autres
Publié: (2025)

Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning
par: Wang, Xinru, et autres
Publié: (2023)

Double-Estimation-Friendly Inference for High-Dimensional Measurement Error Models with Non-Sparse Adaptability
par: Cui, Shijie, et autres
Publié: (2024)

Non-Parametric Bayesian Inference for Partial Orders with Ties from Rank Data observed with Mallows Noise
par: Chuxuan, et autres
Publié: (2024)

Strang Splitting for Parametric Inference in Second-order Stochastic Differential Equations
par: Pilipovic, Predrag, et autres
Publié: (2024)

Segmenting Human-LLM Co-authored Text via Change Point Detection
par: Li, Mengchu, et autres
Publié: (2026)

Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference
par: Dang, Hoang, et autres
Publié: (2026)

Transporting Predictions via Double Machine Learning: Predicting Partially Unobserved Students' Outcomes
par: Bargagli-Stoffi, Falco J., et autres
Publié: (2025)

Beyond the Beta Lorenz Curve: A New Parametric Family for Poverty and Inequality Estimation
par: Sarabia, José María, et autres
Publié: (2026)

Why Is the Double-Robust Estimator for Causal Inference Not Doubly Robust for Variance Estimation?
par: Wu, Hao, et autres
Publié: (2025)