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| 패널 데이터 퍼지 회귀 불연속 설계× | 인과 추론을 위한 도구 변수(IV) 방법× | |
|---|---|---|
| 분야≠ | 인과추론 | 보건경제학 |
| 계열≠ | Regression model | Process / pipeline |
| 기원 연도≠ | 2001 (fuzzy RDD); panel extension circa 2011 | 1990s (modern applications) |
| 창시자≠ | Hahn, Todd & Van der Klaauw; extended to panel settings by Papay, Willett & Murnane and others | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 유형≠ | Quasi-experimental causal inference | Method |
| 원전≠ | 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. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| 별칭 | Panel Fuzzy RDD, Panel FRD, Fuzzy RD with Panel Data, Panel Fuzzy RD | IV, two-stage least squares, TSLS, causal estimation |
| 관련≠ | 5 | 3 |
| 요약≠ | Panel Data Fuzzy Regression Discontinuity Design (Panel FRD) extends the fuzzy RDD framework to settings where multiple observations per unit are available over time. It exploits a probabilistic — rather than deterministic — threshold-crossing rule to identify a local average treatment effect (LATE) while controlling for unit-level and time-level fixed effects, sharpening identification in repeated-measures contexts. | 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. |
| ScholarGate데이터셋 ↗ |
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