Сравнение на методи
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| Контрафактуална оценка на въздействието в изследванията на образованието× | Метод на инструменталните променливи (IV) за причинно-следствен анализ× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Икономика на здравеопазването |
| Семейство≠ | Regression model | Process / pipeline |
| Година на възникване≠ | 2000s–2010s | 1990s (modern applications) |
| Създател≠ | Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research Centre | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Тип≠ | Quasi-experimental causal inference framework | Method |
| Основополагащ източник≠ | Blundell, R., & Costa Dias, M. (2002). Alternative approaches to evaluation in empirical microeconomics. Portuguese Economic Journal, 1(2), 91-115. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Други названия | CIE in education, counterfactual program evaluation, causal impact evaluation, education policy impact evaluation | IV, two-stage least squares, TSLS, causal estimation |
| Свързани≠ | 5 | 3 |
| Резюме≠ | Counterfactual impact evaluation (CIE) is the systematic application of causal inference designs — such as difference-in-differences, regression discontinuity, matching, and instrumental variables — to measure the genuine effect of education programs, policies, or interventions by constructing a credible counterfactual: what would have happened to participants had they not been treated. | 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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