方法对比

	政策评估因果影响分析	中断时间序列（ITS）分析
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2015	2002
提出者	Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
类型	Bayesian counterfactual / time-series	Quasi-experimental segmented regression
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.	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
别名	policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
相关	6	5
摘要	Policy Evaluation Causal Impact Analysis applies the Bayesian structural time-series (BSTS) framework of Brodersen et al. (2015) to estimate the causal effect of a policy intervention on aggregate outcomes. By constructing a synthetic counterfactual from pre-policy data and control covariates, it asks: what would have happened had the policy not been enacted? The difference between observed and predicted post-policy outcomes is the estimated policy effect.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.