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。
| 合成控制法 (SCM)× | 因果影响分析× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2003–2010 | 2015 |
| 提出者≠ | Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010) | Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google) |
| 类型≠ | Quasi-experimental causal inference | Bayesian causal inference / counterfactual forecasting |
| 开创性文献≠ | Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗ | Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274. DOI ↗ |
| 别名 | SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method | CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis |
| 相关≠ | 4 | 5 |
| 摘要≠ | The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect. | Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals. |
| ScholarGate数据集 ↗ |
|
|