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| 공간 퍼지 회귀 불연속 설계× | 인과 추론을 위한 도구 변수(IV) 방법× | |
|---|---|---|
| 분야≠ | 인과추론 | 보건경제학 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 2015 | 1990s (modern applications) |
| 창시자≠ | Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 유형≠ | Quasi-experimental causal inference / IV-based spatial design | Method |
| 원전≠ | Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| 별칭 | Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD | IV, two-stage least squares, TSLS, causal estimation |
| 관련≠ | 5 | 3 |
| 요약≠ | Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic RDD with the instrumental-variable correction for imperfect compliance used in fuzzy RDD. | 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. |
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