Methoden vergleichen

	Bayesian Difference-in-Differences	Kausale Auswirkungsanalyse
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2015-2023	2015
Urheber	Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series)	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Typ	Bayesian causal inference / panel regression	Bayesian causal inference / counterfactual forecasting
Wegweisende Quelle	Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529.	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.
Aliasnamen	Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Verwandt	5	5
Zusammenfassung	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.	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.