Linganisha mbinu

	Uchambuzi wa Athari Husababishi wa Tathmini ya Sera	Tofauti-katika-Tofauti (Diff-in-Diff)
Nyanja	Uhitimisho wa Kisababishi	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2015	1994
Mwanzilishi	Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Aina	Bayesian counterfactual / time-series	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	policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Zinazohusiana	6	5
Muhtasari	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.	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.