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Muwon Kwon

Dados Biográficos

ID8867213
NOMEMuwon Kwon
PRENOMESMuwon
SOBRENOMEKwon
ASSINATURAKWON M
AFILIAÇÕESDepartment of Human Development and Quantitative Methodology, University of Maryland
VERIFICADONão
TOTAL DE OBRAS2
TOTAL DE CITAÇÕES0
OBRAS COMO AUTOR2
OBRAS COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO2026
ANO MAIS RECENTE DE PUBLICAÇÃO2026
ÍNDICE H0
  • Integrating Double/Debiased Machine Learning into Doubly Robust Estimators for Causal Inference

    Muwon Kwon, Peter M Steiner•ARTICLE•Multivariate Behavioral Research•2026

    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

    Monica Morell, Muwon Kwon et al.•ARTICLE•Multivariate Behavioral Research•2026

    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 …

Sem obras proeminentes nesta página.

  • Integrating Double/Debiased Machine Learning into Doubly Robust Estimators for Causal Inference

    Muwon Kwon, Peter M Steiner•ARTICLE•Multivariate Behavioral Research•2026

    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

    Monica Morell, Muwon Kwon et al.•ARTICLE•Multivariate Behavioral Research•2026

    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)

Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae