Salīdzināt metodes

	Heterogeneous Treatment Effect Regression Discontinuity Design	Lokālais vidējais ārstēšanas efekts (LATE / CACE)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2015	1994
Autors	Dong & Lewbel (2015); Chiang, Hsu & Sasaki (2019)	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
Tips	Quasi-experimental causal inference with effect heterogeneity	Instrumental-variable causal estimand
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.	Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.
Citi nosaukumi	HTE-RDD, heterogeneous RDD, subgroup RDD, effect heterogeneity RD	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
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.	The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.