方法对比
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| 空间粗粒化精确匹配 (Spatial CEM)× | 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≠ | 2012 (CEM foundation); spatial extension in applied literature 2015-present | 1983 |
| 提出者≠ | Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians | Paul Rosenbaum and Donald Rubin |
| 类型≠ | Quasi-experimental matching estimator with spatial covariates | Method |
| 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 ↗ | 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 CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates | PSM, propensity score weighting, covariate balance |
| 相关≠ | 6 | 3 |
| 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. | 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. |
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