方法对比

	贝叶斯因果效应分析	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	2015	1994
提出者	Brodersen, Gallusser, Koehler, Remy & Scott (Google)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
类型	Bayesian causal inference / time series	Causal inference / panel 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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
别名	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
相关	4	5
摘要	Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal effect.	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.