手法を比較

	Fuzzy Regression Discontinuity Design	因果推論のための操作変数（IV）法
分野	因果推論	医療経済学
系統	Regression model	Process / pipeline
提唱年	2001	1990s (modern applications)
提唱者	Hahn, Todd & van der Klaauw	Angrist & Pischke (applied econometrics); rooted in econometric theory
種類	Quasi-experimental causal inference	Method
原典	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.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
別名	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD	IV, two-stage least squares, TSLS, causal estimation
関連	5	3
概要	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.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.