Confronta i metodi

	Analisi di Impatto Causale	Serie Temporali Strutturali Bayesiane
Campo	Inferenza causale	Bayesiano
Famiglia	Regression model	Bayesian methods
Anno di origine	2015	2014
Ideatore	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)	Scott & Varian (2014); Brodersen et al. (2015)
Tipo	Bayesian causal inference / counterfactual forecasting	State-space model / Bayesian structural model
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.	Scott, S. L. & Varian, H. R. (2014). Predicting the Present with Bayesian Structural Time Series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1/2), 4–23.
Alias	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model
Correlati	5	5
Sintesi	Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.	Bayesian Structural Time Series (BSTS) is a state-space modelling framework, introduced by Scott and Varian (2014), that decomposes a time series into additive components — trend, seasonality, and regression — and estimates them jointly through Bayesian inference. It underpins Google's CausalImpact library and is a powerful tool for both forecasting and counterfactual causal analysis of interventions.