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| Байесов дизайн на събитийно проучване× | Анализ на причинното въздействие× | |
|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1990s–2010s | 2015 |
| Създател≠ | Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s | Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google) |
| Тип≠ | Quasi-experimental / causal inference | Bayesian causal inference / counterfactual forecasting |
| Основополагащ източник≠ | Sorescu, A., Warren, N. L., & Ertekin, L. (2017). Event study methodology in the marketing literature: An overview. Journal of the Academy of Marketing Science, 45(2), 186-207. DOI ↗ | 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 ↗ |
| Други названия | Bayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BES | CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis |
| Свързани | 5 | 5 |
| Резюме≠ | Bayesian Event Study Design extends the classical event study framework by replacing frequentist significance testing with a full Bayesian inferential framework. It estimates how an event (policy change, announcement, shock) alters an outcome trajectory by learning a prior model from the estimation window and updating it with observed data, yielding posterior distributions over abnormal effects and cumulative causal impacts with full uncertainty quantification. | 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. |
| ScholarGateНабор от данни ↗ |
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