Muwon Kwon
Biographic Data
| ID | 8867213 |
|---|---|
| NAME | Muwon Kwon |
| GIVEN NAMES | Muwon |
| FAMILY NAME | Kwon |
| SIGNATURE | KWON M |
| AFFILIATIONS | Department of Human Development and Quantitative Methodology, University of Maryland |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2026 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 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 …
No prominent works on this page.
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 works) · Bayesian Modeling and Causal Inference (1 work) · Causal inference (1 work) · Causal model (1 work) · Construct (python library) (1 work) · Estimator (1 work) · Explainable Artificial Intelligence (XAI (1 work) · Inference (1 work) · Latent variable (1 work) · Latent variable model (1 work)