So sánh phương pháp

	Phân tích Tác động Nhân quả	Phương pháp Sai phân kép (Difference-in-Differences - DiD)
Lĩnh vực	Suy luận nhân quả	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	2015	1994
Người khởi xướng	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Loại	Bayesian causal inference / counterfactual forecasting	Causal inference / panel regression
Công trình gốc	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
Tên gọi khác	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Liên quan	5	5
Tóm tắt	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.	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.