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| Heterogen Behandlingseffekt Fuzzy Regression Diskontinuitet× | Instrumentalvariabel (IV) Metoden til Kausal Inferens× | |
|---|---|---|
| Fagområde≠ | Kausal inferens | Sundhedsøkonomi |
| Familie≠ | Regression model | Process / pipeline |
| Oprindelsesår≠ | 2001 | 1990s (modern applications) |
| Ophavsperson≠ | Hahn, Todd & Van der Klaauw (2001); extensions by Calonico, Cattaneo & Titiunik (2014) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Type≠ | Quasi-experimental causal inference / heterogeneity analysis | Method |
| Oprindelig kilde≠ | 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. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Aliasser | HTE-Fuzzy RDD, heterogeneous LATE at threshold, subgroup fuzzy RD, fuzzy RD with effect heterogeneity | IV, two-stage least squares, TSLS, causal estimation |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | 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. | 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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