Confronta i metodi

	Analisi di Impatto Causale su Dati Panel	Difference-in-Differences (Diff-in-Diff)
Campo	Inferenza causale	Econometria
Famiglia	Regression model	Regression model
Anno di origine	2015 (base method); panel extension mid-2010s	1994
Ideatore	Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Bayesian structural time-series causal inference	Causal inference / panel regression
Fonte seminale	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
Alias	Panel CausalImpact, multi-unit causal impact, panel BSTS causal inference, panel structural time-series causal analysis	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Correlati	6	5
Sintesi	Panel data causal impact analysis extends the Bayesian structural time-series approach of Brodersen et al. (2015) to multi-unit panel settings, estimating the counterfactual for several treated units simultaneously using control units as a donor pool. It produces credible intervals for the causal effect at each post-intervention time point, aggregated across units and periods.	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.