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| 面板数据双重差分法 (Panel DiD / TWFE)× | 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≠ | 1985–2004 | 1983 |
| 提出者≠ | Ashenfelter & Card (1985); codified by Angrist & Pischke (2009); serial correlation critique by Bertrand, Duflo & Mullainathan (2004) | Paul Rosenbaum and Donald Rubin |
| 类型≠ | Causal inference / panel 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≠ | Two-Way Fixed Effects DiD, TWFE, Panel DiD, Panel Diff-in-Diff | PSM, propensity score weighting, covariate balance |
| 相关≠ | 4 | 3 |
| 摘要≠ | Panel Data Difference-in-Differences extends the classic two-period DiD design to settings with multiple units observed across many time periods. By absorbing unit-level fixed effects and time fixed effects simultaneously, it isolates the causal effect of a treatment or policy change while controlling for both time-invariant unit heterogeneity and common time shocks affecting all units. | 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 ↗ |
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