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| Számszerű hatásbecslés (CIE)× | Instrumentális Változók (IV) Módszer Kauzális Infláció Becslésére× | |
|---|---|---|
| Tudományterület≠ | Oksági következtetés | Egészség-gazdaságtan |
| Módszercsalád≠ | Regression model | Process / pipeline |
| Keletkezés éve≠ | 1970s–2000s | 1990s (modern applications) |
| Megalkotó≠ | Heckman, Imbens, Rubin, and the program evaluation literature | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Típus≠ | Causal inference / program evaluation | Method |
| Alapmű≠ | Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Alternatív nevek | CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation | IV, two-stage least squares, TSLS, causal estimation |
| Kapcsolódó≠ | 5 | 3 |
| Összefoglaló≠ | Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice. | 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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