Linganisha mbinu

	Uchambuzi wa Athari za Kimahesabu za Vipindi Nyingi	Tofauti-katika-Tofauti (Diff-in-Diff)
Nyanja	Uhitimisho wa Kisababishi	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2015 (base); multi-period extensions 2017–present	1994
Mwanzilishi	Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Aina	Bayesian structural time-series / quasi-experimental	Causal inference / panel regression
Chanzo asilia	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
Majina mbadala	multi-period CausalImpact, staggered causal impact, repeated-period causal impact, multi-wave CausalImpact	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Zinazohusiana	6	5
Muhtasari	Multi-period Causal Impact Analysis extends the Bayesian structural time-series framework of Brodersen et al. (2015) to settings where an intervention occurs across multiple distinct periods, is applied at staggered times to different units, or where researchers wish to evaluate cumulative and period-specific effects within a single unified model. It builds a synthetic counterfactual from control covariates and projects it across each intervention window to quantify causal effects.	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.