השוואת שיטות

	ניתוח השפעה סיבתית בייסיאני	שיטת הבקרה הסינתטית (SCM)
תחום	הסקה סיבתית	הסקה סיבתית
משפחה	Regression model	Regression model
שנת המקור	2015	2003–2010
הוגה השיטה	Brodersen, Gallusser, Koehler, Remy & Scott (Google)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
סוג	Bayesian causal inference / time series	Quasi-experimental causal inference
מקור מכונן	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.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
כינויים	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
קשורות	4	4
תקציר	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.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.