Comparar métodos

	Regresión Discontinua con Efectos de Tratamiento Heterogéneos (HTE-RDD)	Diseño difuso de discontinuidad de regresión
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2015	2001
Autor original	Dong & Lewbel (2015); Chiang, Hsu & Sasaki (2019)	Hahn, Todd & van der Klaauw
Tipo	Quasi-experimental causal inference with effect heterogeneity	Quasi-experimental causal inference
Fuente seminal	Dong, Y., & Lewbel, A. (2015). Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models. Review of Economics and Statistics, 97(5), 1081-1092.	Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.
Alias	HTE-RDD, heterogeneous RDD, subgroup RDD, effect heterogeneity RD	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Relacionados	4	5
Resumen	Heterogeneous Treatment Effect RDD extends the classic regression discontinuity framework to detect and estimate how the causal effect of crossing an assignment cutoff varies across subgroups or along covariates. Rather than reporting a single local average treatment effect at the threshold, HTE-RDD maps how treatment impact differs by individual characteristics, enabling richer policy conclusions about who benefits most or least from a threshold-based intervention.	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.