Muwon Kwon
Datos Biográficos
| ID | 8867213 |
|---|---|
| NOMBRE | Muwon Kwon |
| NOMBRES | Muwon |
| APELLIDO | Kwon |
| FIRMA | KWON M |
| AFILIACIONES | Department of Human Development and Quantitative Methodology, University of Maryland |
| VERIFICADO | No |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 0 |
| OBRAS COMO AUTOR | 2 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2026 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 0 |
Integrating Double/Debiased Machine Learning into Doubly Robust Estimators for Causal Inference
When estimating the causal effect of a treatment on an outcome from observational studies, consistent estimates can only be obtained if the unconfoundedness assumption is met (Rosenbaum & Rubin, 19
Regression Discontinuity Analysis with Latent Variables
A regression discontinuity (RD) design is often employed to provide causal evidence when the randomization of the treatment assignment is infeasible. When variables of interest are latent constructs measured by observed indicators, the conventional RD analysis using observed variable scores does not allow researchers to examine heterogeneity in the estimated local average treatment effect (ATE) and to generalize the ATE to participants away from …
Sin obras prominentes en esta página.
Integrating Double/Debiased Machine Learning into Doubly Robust Estimators for Causal Inference
When estimating the causal effect of a treatment on an outcome from observational studies, consistent estimates can only be obtained if the unconfoundedness assumption is met (Rosenbaum & Rubin, 19
Regression Discontinuity Analysis with Latent Variables
A regression discontinuity (RD) design is often employed to provide causal evidence when the randomization of the treatment assignment is infeasible. When variables of interest are latent constructs measured by observed indicators, the conventional RD analysis using observed variable scores does not allow researchers to examine heterogeneity in the estimated local average treatment effect (ATE) and to generalize the ATE to participants away from …
Advanced Causal Inference Techniques (2 obras) · Bayesian Modeling and Causal Inference (1 obra) · Causal inference (1 obra) · Causal model (1 obra) · Construct (python library) (1 obra) · Estimator (1 obra) · Explainable Artificial Intelligence (XAI (1 obra) · Inference (1 obra) · Latent variable (1 obra) · Latent variable model (1 obra)