方法对比
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| 因果推断的工具变量（IV）方法× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 卫生经济学 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s (modern applications) | 1958 |
| 提出者≠ | Angrist & Pischke (applied econometrics); rooted in econometric theory | David Roxbee Cox |
| 类型 | Method | Method |
| 开创性文献≠ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 别名≠ | IV, two-stage least squares, TSLS, causal estimation | logit model, binomial logistic regression, LR |
| 相关 | 3 | 3 |
| 摘要≠ | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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