Σύγκριση μεθόδων

	Ανάλυση Αιτιώδους Επίδρασης Πολλαπλών Περιόδων	Ανάλυση Αιτιώδους Επίπτωσης με Βάση το Bayes
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2015 (base); multi-period extensions 2017–present	2015
Δημιουργός	Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work	Brodersen, Gallusser, Koehler, Remy & Scott (Google)
Τύπος	Bayesian structural time-series / quasi-experimental	Bayesian causal inference / time series
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	multi-period CausalImpact, staggered causal impact, repeated-period causal impact, multi-wave CausalImpact	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis
Συναφείς	6	4
Σύνοψη	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.	Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal effect.