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| 空间回归不连续设计 (Spatial RDD)× | 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≠ | 2010s | 1983 |
| 提出者≠ | Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015) | Paul Rosenbaum and Donald Rubin |
| 类型≠ | Quasi-experimental causal inference | Method |
| 开创性文献≠ | Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903. DOI ↗ | 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≠ | Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design | PSM, propensity score weighting, covariate balance |
| 相关≠ | 4 | 3 |
| 摘要≠ | Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border. | 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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