Vertaile menetelmiä

	Heterogeenisen hoitovaikutuksen regressio-diskontinuiteetti-malli (HTE-RDD)	Paikallinen keskimääräinen vaikutus (LATE / CACE)
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2015	1994
Kehittäjä	Dong & Lewbel (2015); Chiang, Hsu & Sasaki (2019)	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
Tyyppi	Quasi-experimental causal inference with effect heterogeneity	Instrumental-variable causal estimand
Alkuperäislähde	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.
Rinnakkaisnimet	HTE-RDD, heterogeneous RDD, subgroup RDD, effect heterogeneity RD	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
Liittyvät	4	5
Tiivistelmä	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.