Guardado en:
| Autores principales: | Cattaneo, Matias D., Crump, Richard K., Farrell, Max H., Feng, Yingjie |
|---|---|
| Formato: | Preprint |
| Publicado: |
2019
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/1902.09608 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Nonlinear Binscatter Methods
por: Cattaneo, Matias D., et al.
Publicado: (2024)
por: Cattaneo, Matias D., et al.
Publicado: (2024)
Binscatter Regressions
por: Cattaneo, Matias D., et al.
Publicado: (2019)
por: Cattaneo, Matias D., et al.
Publicado: (2019)
Treatment Effect Heterogeneity in Regression Discontinuity Designs
por: Calonico, Sebastian, et al.
Publicado: (2025)
por: Calonico, Sebastian, et al.
Publicado: (2025)
Higher-order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators
por: Cattaneo, Matias D., et al.
Publicado: (2022)
por: Cattaneo, Matias D., et al.
Publicado: (2022)
The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
por: Cattaneo, Matias D., et al.
Publicado: (2025)
por: Cattaneo, Matias D., et al.
Publicado: (2025)
Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
por: Cattaneo, Matias D., et al.
Publicado: (2022)
por: Cattaneo, Matias D., et al.
Publicado: (2022)
The Regression Discontinuity Design in Medical Science
por: Cattaneo, Matias D., et al.
Publicado: (2025)
por: Cattaneo, Matias D., et al.
Publicado: (2025)
The Local Approach to Causal Inference under Network Interference
por: Auerbach, Eric, et al.
Publicado: (2021)
por: Auerbach, Eric, et al.
Publicado: (2021)
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
por: Parikh, Harsh, et al.
Publicado: (2025)
por: Parikh, Harsh, et al.
Publicado: (2025)
Robust Inference for the Direct Average Treatment Effect with Treatment Assignment Interference
por: Cattaneo, Matias D., et al.
Publicado: (2025)
por: Cattaneo, Matias D., et al.
Publicado: (2025)
Improved Inference for CSDID Using the Cluster Jackknife
por: Karim, Sunny R., et al.
Publicado: (2026)
por: Karim, Sunny R., et al.
Publicado: (2026)
Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
por: Nagai, Tomoko, et al.
Publicado: (2024)
por: Nagai, Tomoko, et al.
Publicado: (2024)
Formalising causal inference as prediction on a target population
por: Höltgen, Benedikt, et al.
Publicado: (2024)
por: Höltgen, Benedikt, et al.
Publicado: (2024)
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)
Vector Copula Variational Inference and Dependent Block Posterior Approximations
por: Fu, Yu, et al.
Publicado: (2025)
por: Fu, Yu, et al.
Publicado: (2025)
Robust Matrix Estimation with Side Information
por: Agarwal, Anish, et al.
Publicado: (2026)
por: Agarwal, Anish, et al.
Publicado: (2026)
Fast Online Changepoint Detection
por: Ghezzi, Fabrizio, et al.
Publicado: (2024)
por: Ghezzi, Fabrizio, et al.
Publicado: (2024)
Regularizing Extrapolation in Causal Inference
por: Arbour, David, et al.
Publicado: (2025)
por: Arbour, David, et al.
Publicado: (2025)
Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints
por: Sakaguchi, Shosei
Publicado: (2021)
por: Sakaguchi, Shosei
Publicado: (2021)
Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
por: Si, Nian
Publicado: (2023)
por: Si, Nian
Publicado: (2023)
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)
Machine-Learning-Assisted Comparison of Regression Functions
por: Yan, Jian, et al.
Publicado: (2025)
por: Yan, Jian, et al.
Publicado: (2025)
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
por: Agarwal, Abhineet, et al.
Publicado: (2023)
por: Agarwal, Abhineet, et al.
Publicado: (2023)
An Introduction to Double/Debiased Machine Learning
por: Ahrens, Achim, et al.
Publicado: (2025)
por: Ahrens, Achim, et al.
Publicado: (2025)
Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models
por: Chen, Hongyu, et al.
Publicado: (2026)
por: Chen, Hongyu, et al.
Publicado: (2026)
Linear Multidimensional Regression with Interactive Fixed-Effects
por: Freeman, Hugo
Publicado: (2022)
por: Freeman, Hugo
Publicado: (2022)
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)
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)
HMM-LSTM Fusion Model for Economic Forecasting
por: Sivakumar, Guhan
Publicado: (2025)
por: Sivakumar, Guhan
Publicado: (2025)
Synthetic Potential Outcomes and Causal Mixture Identifiability
por: Mazaheri, Bijan, et al.
Publicado: (2024)
por: Mazaheri, Bijan, et al.
Publicado: (2024)
Re-examining Granger Causality with Causal Bayesian Networks and Reichenbachs Principles
por: Adedayo, S. A.
Publicado: (2025)
por: Adedayo, S. A.
Publicado: (2025)
Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
por: Fuhr, Jonathan, et al.
Publicado: (2024)
por: Fuhr, Jonathan, et al.
Publicado: (2024)
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)
Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
por: Duda, Jarek
Publicado: (2025)
por: Duda, Jarek
Publicado: (2025)
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
por: Zhou, Guanhao, et al.
Publicado: (2025)
por: Zhou, Guanhao, et al.
Publicado: (2025)
A Causal Inference Framework for Data Rich Environments
por: Abadie, Alberto, et al.
Publicado: (2025)
por: Abadie, Alberto, et al.
Publicado: (2025)
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
por: Zhan, Ruohan, et al.
Publicado: (2021)
por: Zhan, Ruohan, et al.
Publicado: (2021)
A Gentle Introduction to Conformal Time Series Forecasting
por: Stocker, M., et al.
Publicado: (2025)
por: Stocker, M., et al.
Publicado: (2025)
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)
Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series
por: Duda, Jarek
Publicado: (2023)
por: Duda, Jarek
Publicado: (2023)
Ejemplares similares
-
Nonlinear Binscatter Methods
por: Cattaneo, Matias D., et al.
Publicado: (2024) -
Binscatter Regressions
por: Cattaneo, Matias D., et al.
Publicado: (2019) -
Treatment Effect Heterogeneity in Regression Discontinuity Designs
por: Calonico, Sebastian, et al.
Publicado: (2025) -
Higher-order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators
por: Cattaneo, Matias D., et al.
Publicado: (2022) -
The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
por: Cattaneo, Matias D., et al.
Publicado: (2025)