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。
| 空间粗粒化精确匹配 (Spatial CEM)× | 空间回归不连续设计 (Spatial RDD)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2012 (CEM foundation); spatial extension in applied literature 2015-present | 2010s |
| 提出者≠ | Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians | Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015) |
| 类型≠ | Quasi-experimental matching estimator with spatial covariates | Quasi-experimental causal inference |
| 开创性文献≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ | Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903. DOI ↗ |
| 别名 | Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates | Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design |
| 相关≠ | 6 | 4 |
| 摘要≠ | Spatial Coarsened Exact Matching applies the Coarsened Exact Matching framework to study designs involving geographic units — neighbourhoods, census tracts, municipalities, or grid cells. Covariates are coarsened into discrete bins and units are matched exactly on those bins, with spatial attributes (location, adjacency, geographic characteristics) incorporated as matching dimensions to control for spatial confounding. | 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. |
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