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。
| 工具变量法/两阶段最小二乘法 (IV/2SLS)× | 倾向得分匹配× | |
|---|---|---|
| 领域≠ | 因果推断 | 研究统计学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2009 | 1983 |
| 提出者≠ | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) | Paul Rosenbaum and Donald Rubin |
| 类型≠ | Instrumental-variables regression | Method |
| 开创性文献≠ | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. DOI ↗ |
| 别名≠ | instrumental variables, IV estimation, 2SLS, instrumental variable regression | PSM, propensity score weighting, covariate balance |
| 相关≠ | 5 | 3 |
| 摘要≠ | IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009). | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
| ScholarGate数据集 ↗ |
|
|