Salīdzināt metodes

	Panel Data Causal Impact Analysis	Panel Data Synthetic Control Method
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2015 (base method); panel extension mid-2010s	2010
Autors	Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature	Alberto Abadie, Alexis Diamond & Jens Hainmueller
Tips	Bayesian structural time-series causal inference	Causal inference / panel data
Pirmavots	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.
Citi nosaukumi	Panel CausalImpact, multi-unit causal impact, panel BSTS causal inference, panel structural time-series causal analysis	SCM panel, panel synthetic control, synthetic control estimator, comparative case study
Saistītās	6	5
Kopsavilkums	Panel data causal impact analysis extends the Bayesian structural time-series approach of Brodersen et al. (2015) to multi-unit panel settings, estimating the counterfactual for several treated units simultaneously using control units as a donor pool. It produces credible intervals for the causal effect at each post-intervention time point, aggregated across units and periods.	The panel data synthetic control method estimates the causal effect of an intervention on a single treated unit by constructing a data-driven weighted combination of untreated units — a synthetic control — that best reproduces the treated unit's pre-treatment outcome trajectory. The post-treatment gap between the treated unit and its synthetic counterpart is the estimated treatment effect.