方法对比
并排查看您选择的方法;存在差异的行会高亮显示。
| 教育研究中的反事实影响评估× | 因果推断的工具变量(IV)方法× | |
|---|---|---|
| 领域≠ | 因果推断 | 卫生经济学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2000s–2010s | 1990s (modern applications) |
| 提出者≠ | Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research Centre | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 类型≠ | Quasi-experimental causal inference framework | Method |
| 开创性文献≠ | 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 ↗ |
| 别名 | CIE in education, counterfactual program evaluation, causal impact evaluation, education policy impact evaluation | IV, two-stage least squares, TSLS, causal estimation |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|