Vertaile menetelmiä

	Kausaalivaikutusanalyysi	Synteettisen kontrollin menetelmä (SCM)
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2015	2003–2010
Kehittäjä	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tyyppi	Bayesian causal inference / counterfactual forecasting	Quasi-experimental causal inference
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.	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.
Rinnakkaisnimet	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Liittyvät	5	4
Tiivistelmä	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.	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.