Сравнение методов

	Регрессионный разрывный дизайн для гетерогенных эффектов воздействия (HTE-RDD)	Локальный средний эффект воздействия (LATE / CACE)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2015	1994
Автор метода	Dong & Lewbel (2015); Chiang, Hsu & Sasaki (2019)	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
Тип	Quasi-experimental causal inference with effect heterogeneity	Instrumental-variable causal estimand
Основополагающий источник	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.
Другие названия	HTE-RDD, heterogeneous RDD, subgroup RDD, effect heterogeneity RD	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
Связанные	4	5
Сводка	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.