Comparar métodos

	Regresión Discontinua con Efectos de Tratamiento Heterogéneos (HTE-RDD)	Regresión Cuantílica
Campo	Inferencia causal	Econometría
Familia	Regression model	Regression model
Año de origen	2015	1978
Autor original	Dong & Lewbel (2015); Chiang, Hsu & Sasaki (2019)	Koenker & Bassett
Tipo	Quasi-experimental causal inference with effect heterogeneity	Conditional quantile regression
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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	HTE-RDD, heterogeneous RDD, subgroup RDD, effect heterogeneity RD	conditional quantile regression, regression quantiles, Kantil Regresyon
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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.