Image de couverture de livre

Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fuhr, Jonathan, Papies, Dominik
Format:	Preprint
Publié:	2024
Sujets:	Econometrics Machine Learning Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
par: Fuhr, Jonathan, et autres
Publié: (2024)

Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
par: Baiardi, Anna, et autres
Publié: (2026)

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

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

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
par: Zhou, Guanhao, et autres
Publié: (2025)

Adaptive Principal Component Regression with Applications to Panel Data
par: Agarwal, Anish, et autres
Publié: (2023)

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
par: Kato, Masahiro, et autres
Publié: (2025)

A Double Machine Learning Approach to Combining Experimental and Observational Data
par: Parikh, Harsh, et autres
Publié: (2023)

Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
par: More, Sushant, et autres
Publié: (2024)

Identification of Average Treatment Effects in Nonparametric Panel Models
par: Athey, Susan, et autres
Publié: (2025)

Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data
par: Langevin, Raphaël
Publié: (2026)

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

Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
par: Chen, Shuyuan, et autres
Publié: (2026)

Robust Inference Methods for Latent Group Panel Models under Possible Group Non-Separation
par: Akgun, Oguzhan, et autres
Publié: (2025)

Machine-Learning-Assisted Comparison of Regression Functions
par: Yan, Jian, et autres
Publié: (2025)

Double Robust Bayesian Inference on Average Treatment Effects
par: Breunig, Christoph, et autres
Publié: (2022)

Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach
par: Yan, Yuling, et autres
Publié: (2024)

Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
par: Kato, Masahiro, et autres
Publié: (2023)

Data-Automated Policy Learning for Nonlinear Welfare
par: Ai, Chunrong, et autres
Publié: (2026)

Synthetic Potential Outcomes and Causal Mixture Identifiability
par: Mazaheri, Bijan, et autres
Publié: (2024)

Distributed Causality in the SDG Network: Evidence from Panel VAR and Conditional Independence Analysis
par: Fahim, Md Muhtasim Munif, et autres
Publié: (2026)

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
par: Athey, Susan, et autres
Publié: (2023)

Optimal Bias-Correction and Valid Inference in High-Dimensional Ridge Regression: A Closed-Form Solution
par: Gao, Zhaoxing, et autres
Publié: (2024)

Multiply-Robust Causal Change Attribution
par: Quintas-Martinez, Victor, et autres
Publié: (2024)

Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning
par: Rombouts, Jeroen, et autres
Publié: (2024)

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

Data Fusion for Partial Identification of Causal Effects
par: Lanners, Quinn, et autres
Publié: (2025)

Estimation and Inference for Causal Functions with Multiway Clustered Data
par: Liu, Nan, et autres
Publié: (2024)

A Causal Inference Framework for Data Rich Environments
par: Abadie, Alberto, et autres
Publié: (2025)

Double Machine Learning for Static Panel Models with Fixed Effects
par: Clarke, Paul S., et autres
Publié: (2023)

Deep Learning With DAGs
par: Balgi, Sourabh, et autres
Publié: (2024)

Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
par: Zhan, Ruohan, et autres
Publié: (2021)

Long-term Causal Inference Under Persistent Confounding via Data Combination
par: Imbens, Guido, et autres
Publié: (2022)

Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
par: Yang, Xuelin, et autres
Publié: (2025)

Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
par: Chen, Hongyu, et autres
Publié: (2026)

STEEL: Singularity-aware Reinforcement Learning
par: Chen, Xiaohong, et autres
Publié: (2023)

Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
par: Si, Nian
Publié: (2023)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects
par: Kallus, Nathan, et autres
Publié: (2022)

Individualized Policy Evaluation and Learning under Clustered Network Interference
par: Zhang, Yi, et autres
Publié: (2023)

Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction
par: Zeng, Hao, et autres
Publié: (2024)