Krahasoni metodat

	Vlerësimi Bajezian i Ndikimit Kunderfaktual	Diferencat-në-Diferenca Bayesiane
Fusha	Inferenca kauzale	Inferenca kauzale
Familja	Regression model	Regression model
Viti i origjinës	2015 (canonical implementation); Rubin potential outcomes: 1974-2005	2015-2023
Krijuesi	Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework	Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series)
Lloji	Bayesian causal inference / counterfactual estimation	Bayesian causal inference / panel regression
Burimi themelues	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.	Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529.
Emërtime të tjera	Bayesian CIE, Bayesian causal impact, Bayesian structural time-series causal inference, BSTS counterfactual evaluation	Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator
Të lidhura	5	5
Përmbledhja	Bayesian Counterfactual Impact Evaluation estimates the causal effect of an intervention by constructing a Bayesian posterior distribution over the counterfactual outcome — what would have happened without treatment. The method, popularized by Brodersen et al. (2015) through the CausalImpact framework, uses Bayesian structural time-series models fitted on the pre-intervention period to predict the counterfactual trajectory, then compares observed post-intervention outcomes to that prediction.	Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available.