Võrdle meetodeid

	Masinõppega täiendatud regressiooni katkestuspunkti disain	Fuzzy Regression Discontinuity Design
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2019	2001
Looja	Imbens & Wager (2019); Calonico, Cattaneo & Farrell (2019)	Hahn, Todd & van der Klaauw
Tüüp	Causal inference / quasi-experimental	Quasi-experimental causal inference
Algallikas	Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). Optimal mean squared error bandwidth selection for regression discontinuity designs. Bernoulli, 25(4A), 2703-2729.	Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.
Rööpnimetused	ML-RDD, ML-augmented RD, data-adaptive RDD, nonparametric RDD with ML	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Seotud	3	5
Kokkuvõte	Machine learning-augmented regression discontinuity design (ML-RDD) combines the sharp identification logic of classical RDD — exploiting a known assignment cutoff in a running variable — with flexible, data-adaptive ML methods for bandwidth selection, conditional mean estimation, and covariate adjustment. The goal is to recover a more accurate and less assumption-laden estimate of the local average treatment effect at the threshold.	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.