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| Bayesian Counterfactual Impact Evaluation× | Bayesov dizajn razlike u razlikama× | |
|---|---|---|
| Područje | Uzročno zaključivanje | Uzročno zaključivanje |
| Obitelj | Regression model | Regression model |
| Godina nastanka≠ | 2015 (canonical implementation); Rubin potential outcomes: 1974-2005 | 2015-2023 |
| Tvorac≠ | Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework | Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series) |
| Vrsta≠ | Bayesian causal inference / counterfactual estimation | Bayesian causal inference / panel regression |
| Temeljni izvor≠ | 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. DOI ↗ | Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529. link ↗ |
| Drugi nazivi | 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 |
| Srodne | 5 | 5 |
| Sažetak≠ | 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. |
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