方法对比
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| 政策评估安慰剂检验× | 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≠ | 1990s–2000s | 1990s (modern applications) |
| 提出者≠ | Bertrand, Duflo & Mullainathan (2004 canonical formalization); Imbens & Wooldridge (2009 textbook treatment) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 类型≠ | Falsification / specification check | Method |
| 开创性文献≠ | Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| 别名 | placebo test, falsification test, fake treatment test, placebo regression | IV, two-stage least squares, TSLS, causal estimation |
| 相关≠ | 4 | 3 |
| 摘要≠ | A policy evaluation placebo test is a falsification check used in quasi-experimental research to validate a causal identification strategy. The researcher applies the same estimation method to a pseudo-treatment — a time period, group, or outcome where the real policy could not have had an effect — and checks that no spurious effect is detected. A null placebo result builds confidence that the main estimate reflects a genuine causal impact rather than bias or confounding. | 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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