Salīdzināt metodes

	Heterogeneous Treatment Effect Fuzzy RDD	Lokālais vidējais ārstēšanas efekts (LATE / CACE)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2001	1994
Autors	Hahn, Todd & Van der Klaauw (2001); extensions by Calonico, Cattaneo & Titiunik (2014)	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
Tips	Quasi-experimental causal inference / heterogeneity analysis	Instrumental-variable causal estimand
Pirmavots	Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Econometrica, 69(1), 201-209.	Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.
Citi nosaukumi	HTE-Fuzzy RDD, heterogeneous LATE at threshold, subgroup fuzzy RD, fuzzy RD with effect heterogeneity	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
Saistītās	5	5
Kopsavilkums	Heterogeneous Treatment Effect Fuzzy RDD extends the standard fuzzy regression discontinuity design — where treatment probability, not treatment status itself, jumps at a threshold — by examining whether the Local Average Treatment Effect (LATE) estimated at the threshold differs systematically across subgroups defined by covariates such as gender, socioeconomic status, or prior ability. It combines the instrumental-variable logic of fuzzy RDD with structured heterogeneity analysis.	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.