xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
Fuente:
arXiv
Guardado en:
| Autor principal: | Polselli, Annalivia |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Double Machine Learning for Static Panel Models with Fixed Effects
por: Clarke, Paul S., et al.
Publicado: (2023)
por: Clarke, Paul S., et al.
Publicado: (2023)
Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
por: Baiardi, Anna, et al.
Publicado: (2026)
por: Baiardi, Anna, et al.
Publicado: (2026)
Robust Inference in Panel Data Models: Some Effects of Heteroskedasticity and Leveraged Data in Small Samples
por: Polselli, Annalivia
Publicado: (2023)
por: Polselli, Annalivia
Publicado: (2023)
Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
por: Fuhr, Jonathan, et al.
Publicado: (2024)
por: Fuhr, Jonathan, et al.
Publicado: (2024)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
por: Fuhr, Jonathan, et al.
Publicado: (2024)
por: Fuhr, Jonathan, et al.
Publicado: (2024)
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
por: Zhou, Guanhao, et al.
Publicado: (2025)
por: Zhou, Guanhao, et al.
Publicado: (2025)
Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models
por: Kwon, Soonwoo
Publicado: (2023)
por: Kwon, Soonwoo
Publicado: (2023)
An Introduction to Double/Debiased Machine Learning
por: Ahrens, Achim, et al.
Publicado: (2025)
por: Ahrens, Achim, et al.
Publicado: (2025)
Identification of Average Treatment Effects in Nonparametric Panel Models
por: Athey, Susan, et al.
Publicado: (2025)
por: Athey, Susan, et al.
Publicado: (2025)
Adaptive Principal Component Regression with Applications to Panel Data
por: Agarwal, Anish, et al.
Publicado: (2023)
por: Agarwal, Anish, et al.
Publicado: (2023)
Tractable Estimation of Nonlinear Panels with Interactive Fixed Effects
por: Zeleneev, Andrei, et al.
Publicado: (2025)
por: Zeleneev, Andrei, et al.
Publicado: (2025)
Robust Estimation and Inference in Panels with Interactive Fixed Effects
por: Armstrong, Timothy B., et al.
Publicado: (2022)
por: Armstrong, Timothy B., et al.
Publicado: (2022)
Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data
por: Langevin, Raphaël
Publicado: (2026)
por: Langevin, Raphaël
Publicado: (2026)
Linear Multidimensional Regression with Interactive Fixed-Effects
por: Freeman, Hugo
Publicado: (2022)
por: Freeman, Hugo
Publicado: (2022)
Bootstrap Inference in Nonlinear Panel Data Models with Interactive Fixed Effects
por: Xu, Haoyuan, et al.
Publicado: (2026)
por: Xu, Haoyuan, et al.
Publicado: (2026)
Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
por: Chen, Shuyuan, et al.
Publicado: (2026)
por: Chen, Shuyuan, et al.
Publicado: (2026)
A Double Machine Learning Approach to Combining Experimental and Observational Data
por: Parikh, Harsh, et al.
Publicado: (2023)
por: Parikh, Harsh, et al.
Publicado: (2023)
Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
por: Kato, Masahiro, et al.
Publicado: (2023)
por: Kato, Masahiro, et al.
Publicado: (2023)
Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
por: More, Sushant, et al.
Publicado: (2024)
por: More, Sushant, et al.
Publicado: (2024)
Double Robust Bayesian Inference on Average Treatment Effects
por: Breunig, Christoph, et al.
Publicado: (2022)
por: Breunig, Christoph, et al.
Publicado: (2022)
Identification of Dynamic Panel Logit Models with Fixed Effects
por: Dobronyi, Christopher, et al.
Publicado: (2021)
por: Dobronyi, Christopher, et al.
Publicado: (2021)
Robust Inference Methods for Latent Group Panel Models under Possible Group Non-Separation
por: Akgun, Oguzhan, et al.
Publicado: (2025)
por: Akgun, Oguzhan, et al.
Publicado: (2025)
Estimating Dyadic Treatment Effects with Unknown Confounders
por: Hoshino, Tadao, et al.
Publicado: (2024)
por: Hoshino, Tadao, et al.
Publicado: (2024)
Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
por: Dalal, Abhinandan, et al.
Publicado: (2024)
por: Dalal, Abhinandan, et al.
Publicado: (2024)
Estimation and Inference for Causal Functions with Multiway Clustered Data
por: Liu, Nan, et al.
Publicado: (2024)
por: Liu, Nan, et al.
Publicado: (2024)
Machine-Learning-Assisted Comparison of Regression Functions
por: Yan, Jian, et al.
Publicado: (2025)
por: Yan, Jian, et al.
Publicado: (2025)
Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
por: Kato, Masahiro, et al.
Publicado: (2024)
por: Kato, Masahiro, et al.
Publicado: (2024)
Data Fusion for Partial Identification of Causal Effects
por: Lanners, Quinn, et al.
Publicado: (2025)
por: Lanners, Quinn, et al.
Publicado: (2025)
Estimating Wage Disparities Using Foundation Models
por: Vafa, Keyon, et al.
Publicado: (2024)
por: Vafa, Keyon, et al.
Publicado: (2024)
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
por: Athey, Susan, et al.
Publicado: (2016)
por: Athey, Susan, et al.
Publicado: (2016)
Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach
por: Zhan, Ruohan, et al.
Publicado: (2024)
por: Zhan, Ruohan, et al.
Publicado: (2024)
Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
por: Kato, Masahiro, et al.
Publicado: (2025)
por: Kato, Masahiro, et al.
Publicado: (2025)
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach
por: Yan, Yuling, et al.
Publicado: (2024)
por: Yan, Yuling, et al.
Publicado: (2024)
Data-Automated Policy Learning for Nonlinear Welfare
por: Ai, Chunrong, et al.
Publicado: (2026)
por: Ai, Chunrong, et al.
Publicado: (2026)
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
por: Kallus, Nathan, et al.
Publicado: (2022)
por: Kallus, Nathan, et al.
Publicado: (2022)
Distributed Causality in the SDG Network: Evidence from Panel VAR and Conditional Independence Analysis
por: Fahim, Md Muhtasim Munif, et al.
Publicado: (2026)
por: Fahim, Md Muhtasim Munif, et al.
Publicado: (2026)
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
por: Athey, Susan, et al.
Publicado: (2023)
por: Athey, Susan, et al.
Publicado: (2023)
Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions
por: Faletto, Gregory
Publicado: (2023)
por: Faletto, Gregory
Publicado: (2023)
Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators
por: Klaassen, Sven, et al.
Publicado: (2025)
por: Klaassen, Sven, et al.
Publicado: (2025)
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
por: Ghassami, AmirEmad, et al.
Publicado: (2022)
por: Ghassami, AmirEmad, et al.
Publicado: (2022)
Ejemplares similares
-
Double Machine Learning for Static Panel Models with Fixed Effects
por: Clarke, Paul S., et al.
Publicado: (2023) -
Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
por: Baiardi, Anna, et al.
Publicado: (2026) -
Robust Inference in Panel Data Models: Some Effects of Heteroskedasticity and Leveraged Data in Small Samples
por: Polselli, Annalivia
Publicado: (2023) -
Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
por: Fuhr, Jonathan, et al.
Publicado: (2024) -
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
por: Fuhr, Jonathan, et al.
Publicado: (2024)