Methoden vergleichen

	Bayesian Counterfactual Impact Evaluation	Differenz-in-Differenzen (DiD)
Fachgebiet	Kausale Inferenz	Ökonometrie
Familie	Regression model	Regression model
Entstehungsjahr	2015 (canonical implementation); Rubin potential outcomes: 1974-2005	1994
Urheber	Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Typ	Bayesian causal inference / counterfactual estimation	Causal inference / panel regression
Wegweisende Quelle	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
Aliasnamen	Bayesian CIE, Bayesian causal impact, Bayesian structural time-series causal inference, BSTS counterfactual evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwandt	5	5
Zusammenfassung	Bayesian Counterfactual Impact Evaluation estimates the causal effect of an intervention by constructing a Bayesian posterior distribution over the counterfactual outcome — what would have happened without treatment. The method, popularized by Brodersen et al. (2015) through the CausalImpact framework, uses Bayesian structural time-series models fitted on the pre-intervention period to predict the counterfactual trajectory, then compares observed post-intervention outcomes to that prediction.	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.