Saved in:
| Main Author: | Muny, Fabian |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.11960 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Fairness-Aware and Interpretable Policy Learning
by: Bearth, Nora, et al.
Published: (2025)
by: Bearth, Nora, et al.
Published: (2025)
Peer Effects in Labor Market Training
by: Unterhofer, Ulrike
Published: (2022)
by: Unterhofer, Ulrike
Published: (2022)
Party On: The Labor Market Returns to Social Networks in Adolescence
by: Lleras-Muney, Adriana, et al.
Published: (2022)
by: Lleras-Muney, Adriana, et al.
Published: (2022)
Double Machine Learning for Time Series
by: Ciganovic, Milos, et al.
Published: (2026)
by: Ciganovic, Milos, et al.
Published: (2026)
Bayesian Double Machine Learning for Causal Inference
by: DiTraglia, Francis J., et al.
Published: (2025)
by: DiTraglia, Francis J., et al.
Published: (2025)
Estimating Heterogeneous Effects: Applications to Labor Economics
by: Bonhomme, Stephane, et al.
Published: (2024)
by: Bonhomme, Stephane, et al.
Published: (2024)
CAREER: A Foundation Model for Labor Sequence Data
by: Vafa, Keyon, et al.
Published: (2022)
by: Vafa, Keyon, et al.
Published: (2022)
Model Averaging and Double Machine Learning
by: Achim Ahrens, et al.
Published: (2025)
by: Achim Ahrens, et al.
Published: (2025)
Model Averaging and Double Machine Learning
by: Ahrens, Achim, et al.
Published: (2024)
by: Ahrens, Achim, et al.
Published: (2024)
The Persistent Effects of Peru's Mining MITA: Double Machine Learning Approach
by: Karakas, Alper Deniz
Published: (2025)
by: Karakas, Alper Deniz
Published: (2025)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
by: Fuhr, Jonathan, et al.
Published: (2024)
by: Fuhr, Jonathan, et al.
Published: (2024)
Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets
by: Proz, Jeremy, et al.
Published: (2025)
by: Proz, Jeremy, et al.
Published: (2025)
Neighborhood Stability in Double/Debiased Machine Learning with Dependent Data
by: Cao, Jianfei, et al.
Published: (2025)
by: Cao, Jianfei, et al.
Published: (2025)
Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes
by: Fernandez-Val, Ivan, et al.
Published: (2022)
by: Fernandez-Val, Ivan, et al.
Published: (2022)
Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications
by: Baiardi, Anna, et al.
Published: (2026)
by: Baiardi, Anna, et al.
Published: (2026)
Modelling and Forecasting Energy Market Volatility Using GARCH and Machine Learning Approach
by: Chung, Seulki
Published: (2024)
by: Chung, Seulki
Published: (2024)
An Introduction to Double/Debiased Machine Learning
by: Ahrens, Achim, et al.
Published: (2025)
by: Ahrens, Achim, et al.
Published: (2025)
On the Anchoring Effect of Monetary Policy on the Labor Share of Income and the Rationality of Its Setting Mechanism
by: Tuobang, Li
Published: (2026)
by: Tuobang, Li
Published: (2026)
Double Machine Learning for Static Panel Models with Fixed Effects
by: Clarke, Paul S., et al.
Published: (2023)
by: Clarke, Paul S., et al.
Published: (2023)
Machine Learning Debiasing with Conditional Moment Restrictions: An Application to LATE
by: Argañaraz, Facundo, et al.
Published: (2024)
by: Argañaraz, Facundo, et al.
Published: (2024)
DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
by: Bach, Philipp, et al.
Published: (2021)
by: Bach, Philipp, et al.
Published: (2021)
Double Machine Learning for Causal Inference under Shared-State Interference
by: Hays, Chris, et al.
Published: (2025)
by: Hays, Chris, et al.
Published: (2025)
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
by: Bach, Philipp, et al.
Published: (2024)
by: Bach, Philipp, et al.
Published: (2024)
Evaluating Counterfactual Policies Using Instruments
by: Kolesár, Michal, et al.
Published: (2025)
by: Kolesár, Michal, et al.
Published: (2025)
Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning
by: Haddad, Michel F. C., et al.
Published: (2024)
by: Haddad, Michel F. C., et al.
Published: (2024)
Double/Debiased Machine Learning for Treatment and Causal Parameters
by: Chernozhukov, Victor, et al.
Published: (2016)
by: Chernozhukov, Victor, et al.
Published: (2016)
SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement
by: Cho, Brian, et al.
Published: (2025)
by: Cho, Brian, et al.
Published: (2025)
Unleashing the Power of AI: Transforming Marketing Decision-Making in Heavy Machinery with Machine Learning, Radar Chart Simulation, and Markov Chain Analysis
by: Tian, Tian, et al.
Published: (2024)
by: Tian, Tian, et al.
Published: (2024)
Bayesian Indicator-Saturated Regression for Climate Policy Evaluation
by: Konrad, Lucas D., et al.
Published: (2026)
by: Konrad, Lucas D., et al.
Published: (2026)
Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
by: Fuhr, Jonathan, et al.
Published: (2024)
by: Fuhr, Jonathan, et al.
Published: (2024)
Policy Learning with New Treatments
by: Higbee, Samuel
Published: (2022)
by: Higbee, Samuel
Published: (2022)
Nonparametric Bayesian Policy Learning
by: Ye, Haonan
Published: (2026)
by: Ye, Haonan
Published: (2026)
From Clerks to Agentic-AI: How will Technology Change Labor Market in Finance?
by: Yu, Lu, et al.
Published: (2026)
by: Yu, Lu, et al.
Published: (2026)
Fast Learning of Optimal Policy Trees
by: Cussens, James, et al.
Published: (2025)
by: Cussens, James, et al.
Published: (2025)
Policy Learning with $α$-Expected Welfare
by: Fan, Yanqin, et al.
Published: (2025)
by: Fan, Yanqin, et al.
Published: (2025)
Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
by: More, Sushant, et al.
Published: (2024)
by: More, Sushant, et al.
Published: (2024)
Evaluating the Impact of Regulatory Policies on Social Welfare in Difference-in-Difference Settings
by: Ghanem, Dalia, et al.
Published: (2023)
by: Ghanem, Dalia, et al.
Published: (2023)
Policy Learning with Confidence
by: Chernozhukov, Victor, et al.
Published: (2025)
by: Chernozhukov, Victor, et al.
Published: (2025)
Comprehensive Causal Machine Learning
by: Lechner, Michael, et al.
Published: (2024)
by: Lechner, Michael, et al.
Published: (2024)
Who With Whom? Learning Optimal Matching Policies
by: Hazard, Yagan, et al.
Published: (2025)
by: Hazard, Yagan, et al.
Published: (2025)
Similar Items
-
Fairness-Aware and Interpretable Policy Learning
by: Bearth, Nora, et al.
Published: (2025) -
Peer Effects in Labor Market Training
by: Unterhofer, Ulrike
Published: (2022) -
Party On: The Labor Market Returns to Social Networks in Adolescence
by: Lleras-Muney, Adriana, et al.
Published: (2022) -
Double Machine Learning for Time Series
by: Ciganovic, Milos, et al.
Published: (2026) -
Bayesian Double Machine Learning for Causal Inference
by: DiTraglia, Francis J., et al.
Published: (2025)