ddml: Double/debiased machine learning in Stata
Fuente:
arXiv
Guardado en:
| Autores principales: | Ahrens, Achim, Hansen, Christian B., Schaffer, Mark E., Wiemann, Thomas |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Model Averaging and Double Machine Learning
por: Ahrens, Achim, et al.
Publicado: (2024)
por: Ahrens, Achim, et al.
Publicado: (2024)
An Introduction to Double/Debiased Machine Learning
por: Ahrens, Achim, et al.
Publicado: (2025)
por: Ahrens, Achim, et al.
Publicado: (2025)
Model Averaging and Double Machine Learning
por: Achim Ahrens, et al.
Publicado: (2025)
por: Achim Ahrens, et al.
Publicado: (2025)
Automatic debiased machine learning and sensitivity analysis for sample selection models
por: Bjelac, Jakob, et al.
Publicado: (2026)
por: Bjelac, Jakob, et al.
Publicado: (2026)
Optimal Categorical Instrumental Variables
por: Wiemann, Thomas
Publicado: (2023)
por: Wiemann, Thomas
Publicado: (2023)
Semiparametric inference for impulse response functions using double/debiased machine learning
por: Ballinari, Daniele, et al.
Publicado: (2024)
por: Ballinari, Daniele, et al.
Publicado: (2024)
Continuous difference-in-differences with double/debiased machine learning
por: Zhang, Lucas Z.
Publicado: (2024)
por: Zhang, Lucas Z.
Publicado: (2024)
Identifying factors via automatic debiased machine learning
por: Esfandiar Maasoumi, et al.
Publicado: (2024)
por: Esfandiar Maasoumi, et al.
Publicado: (2024)
Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability
por: Rehill, Patrick, et al.
Publicado: (2023)
por: Rehill, Patrick, et al.
Publicado: (2023)
Bootstrap consistency for general double/debiased machine learning estimators
por: Lin, Ziming, et al.
Publicado: (2026)
por: Lin, Ziming, et al.
Publicado: (2026)
Double/Debiased Machine Learning for Treatment and Causal Parameters
por: Chernozhukov, Victor, et al.
Publicado: (2016)
por: Chernozhukov, Victor, et al.
Publicado: (2016)
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
por: Wang, Shenhao, et al.
Publicado: (2021)
por: Wang, Shenhao, et al.
Publicado: (2021)
Functional effects models: Accounting for preference heterogeneity in panel data with machine learning
por: Salvadé, Nicolas, et al.
Publicado: (2025)
por: Salvadé, Nicolas, et al.
Publicado: (2025)
A step towards the integration of machine learning and classic model-based survey methods
por: Żądło, Tomasz, et al.
Publicado: (2024)
por: Żądło, Tomasz, et al.
Publicado: (2024)
Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso
por: Hué, Sullivan, et al.
Publicado: (2025)
por: Hué, Sullivan, et al.
Publicado: (2025)
The Post Double LASSO for Efficiency Analysis
por: Parmeter, Christopher, et al.
Publicado: (2025)
por: Parmeter, Christopher, et al.
Publicado: (2025)
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 Causal Inference under Shared-State Interference
por: Hays, Chris, et al.
Publicado: (2025)
por: Hays, Chris, et al.
Publicado: (2025)
Inference for Regression with Variables Generated by AI or Machine Learning
por: Battaglia, Laura, et al.
Publicado: (2024)
por: Battaglia, Laura, et al.
Publicado: (2024)
Applied Causal Inference Powered by ML and AI
por: Chernozhukov, Victor, et al.
Publicado: (2024)
por: Chernozhukov, Victor, et al.
Publicado: (2024)
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
por: Bach, Philipp, et al.
Publicado: (2024)
por: Bach, Philipp, et al.
Publicado: (2024)
Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning
por: Haddad, Michel F. C., et al.
Publicado: (2024)
por: Haddad, Michel F. C., et al.
Publicado: (2024)
Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration
por: Ballinari, Daniele, et al.
Publicado: (2024)
por: Ballinari, Daniele, 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)
biastest: Testing parameter equality across different models in Stata
por: Guliyev, Hasraddin
Publicado: (2025)
por: Guliyev, Hasraddin
Publicado: (2025)
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
por: Bach, Philipp, et al.
Publicado: (2021)
por: Bach, Philipp, et al.
Publicado: (2021)
Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
por: Kato, Masahiro, et al.
Publicado: (2023)
por: Kato, Masahiro, et al.
Publicado: (2023)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
por: Fuhr, Jonathan, et al.
Publicado: (2024)
por: Fuhr, Jonathan, et al.
Publicado: (2024)
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)
Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies
por: Panat, Tanay, et al.
Publicado: (2025)
por: Panat, Tanay, et al.
Publicado: (2025)
Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata
por: Ditzen, Jan, et al.
Publicado: (2021)
por: Ditzen, Jan, et al.
Publicado: (2021)
$\texttt{rdid}$ and $\texttt{rdidstag}$: Stata commands for robust difference-in-differences
por: Ban, Kyunghoon, et al.
Publicado: (2024)
por: Ban, Kyunghoon, et al.
Publicado: (2024)
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)
xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
por: Polselli, Annalivia
Publicado: (2025)
por: Polselli, Annalivia
Publicado: (2025)
fmeffects: An R Package for Forward Marginal Effects
por: Löwe, Holger, et al.
Publicado: (2023)
por: Löwe, Holger, et al.
Publicado: (2023)
Optimal Policy Learning for Multi-Action Treatment with Risk Preference using Stata
por: Cerulli, Giovanni
Publicado: (2025)
por: Cerulli, Giovanni
Publicado: (2025)
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)
PySDTest: a Python/Stata Package for Stochastic Dominance Tests
por: Lee, Kyungho, et al.
Publicado: (2023)
por: Lee, Kyungho, et al.
Publicado: (2023)
Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
por: Hazenberg, Thomas, et al.
Publicado: (2025)
por: Hazenberg, Thomas, et al.
Publicado: (2025)
The boosted HP filter is more general than you might think
por: Mei, Ziwei, et al.
Publicado: (2022)
por: Mei, Ziwei, et al.
Publicado: (2022)
Ejemplares similares
-
Model Averaging and Double Machine Learning
por: Ahrens, Achim, et al.
Publicado: (2024) -
An Introduction to Double/Debiased Machine Learning
por: Ahrens, Achim, et al.
Publicado: (2025) -
Model Averaging and Double Machine Learning
por: Achim Ahrens, et al.
Publicado: (2025) -
Automatic debiased machine learning and sensitivity analysis for sample selection models
por: Bjelac, Jakob, et al.
Publicado: (2026) -
Optimal Categorical Instrumental Variables
por: Wiemann, Thomas
Publicado: (2023)