Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Discontinuitate de Regresie Fuzzy Bayesiană× | Metoda Variabilelor Instrumentale (IV) pentru Inferența Cauzală× | |
|---|---|---|
| Domeniu≠ | Inferență cauzală | Economia sănătății |
| Familie≠ | Regression model | Process / pipeline |
| Anul apariției≠ | 2001 (fuzzy RD identification); 2016 (Bayesian formulation by Chib & Jacobi) | 1990s (modern applications) |
| Autorul original≠ | Chib & Jacobi (Bayesian formulation); Hahn, Todd & Van der Klaauw (fuzzy RD identification) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Tip≠ | Bayesian causal inference / quasi-experimental design | Method |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative≠ | Bayesian Fuzzy RD, Bayesian Fuzzy RDD, Fuzzy RD with Bayesian Inference | IV, two-stage least squares, TSLS, causal estimation |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | Bayesian Fuzzy Regression Discontinuity (Bayesian Fuzzy RD) combines the quasi-experimental logic of fuzzy regression discontinuity design with full Bayesian inference. It estimates a local average treatment effect at a policy threshold where treatment assignment is probabilistic rather than deterministic, placing prior distributions over all unknowns and recovering a complete posterior distribution of the causal effect rather than a single point estimate. | 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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