方法对比

	贝叶斯反事实影响评估	合成控制法 (SCM)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2015 (canonical implementation); Rubin potential outcomes: 1974-2005	2003–2010
提出者	Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
类型	Bayesian causal inference / counterfactual estimation	Quasi-experimental causal inference
开创性文献	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.	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.
别名	Bayesian CIE, Bayesian causal impact, Bayesian structural time-series causal inference, BSTS counterfactual evaluation	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
相关	5	4
摘要	Bayesian Counterfactual Impact Evaluation estimates the causal effect of an intervention by constructing a Bayesian posterior distribution over the counterfactual outcome — what would have happened without treatment. The method, popularized by Brodersen et al. (2015) through the CausalImpact framework, uses Bayesian structural time-series models fitted on the pre-intervention period to predict the counterfactual trajectory, then compares observed post-intervention outcomes to that prediction.	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.