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)× | 双重差分法 (Diff-in-Diff)× | |
|---|---|---|
| 领域≠ | 因果推断 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2012 (CEM foundation); spatial extension in applied literature 2015-present | 1994 |
| 提出者≠ | Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| 类型≠ | Quasi-experimental matching estimator with spatial covariates | Causal inference / panel regression |
| 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 ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 别名≠ | Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| 相关≠ | 6 | 5 |
| 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. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
| ScholarGate数据集 ↗ |
|
|