Vertaile menetelmiä

	Politiikan arviointi: kausaalisen vaikutuksen analyysi	Bayesiläinen kausaalivaikutusanalyysi
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2015	2015
Kehittäjä	Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts	Brodersen, Gallusser, Koehler, Remy & Scott (Google)
Tyyppi	Bayesian counterfactual / time-series	Bayesian causal inference / time series
Alkuperäislähde	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.
Rinnakkaisnimet	policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis
Liittyvät	6	4
Tiivistelmä	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.	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.