Salīdzināt metodes

	Heterogeneous Treatment Effect Regression Discontinuity Design	Kvantīļu regresija
Nozare	Cēloņsakarību secināšana	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2015	1978
Autors	Dong & Lewbel (2015); Chiang, Hsu & Sasaki (2019)	Koenker & Bassett
Tips	Quasi-experimental causal inference with effect heterogeneity	Conditional quantile regression
Pirmavots	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.
Citi nosaukumi	HTE-RDD, heterogeneous RDD, subgroup RDD, effect heterogeneity RD	conditional quantile regression, regression quantiles, Kantil Regresyon
Saistītās	4	5
Kopsavilkums	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.