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| Bayes-féle Placebo Teszt× | Bayesian Causal Impact Analysis× | |
|---|---|---|
| Tudományterület | Oksági következtetés | Oksági következtetés |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 2010-2015 | 2015 |
| Megalkotó≠ | Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition) | Brodersen, Gallusser, Koehler, Remy & Scott (Google) |
| Típus≠ | Robustness check / falsification test | Bayesian causal inference / time series |
| Alapmű | 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 ↗ | 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 ↗ |
| Alternatív nevek | Bayesian falsification test, Bayesian permutation placebo, Bayesian robustness check, Bayesian in-time placebo | CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis |
| Kapcsolódó≠ | 5 | 4 |
| Összefoglaló≠ | The Bayesian Placebo Test is a falsification strategy for causal inference that applies Bayesian inference to placebo scenarios — either fake treatments in the pre-intervention period, on unaffected units, or at fictitious cut-offs — to verify that observed treatment effects cannot plausibly arise by chance or from a misspecified model. It integrates prior information and yields posterior distributions of placebo effects for direct probabilistic comparison. | Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal effect. |
| ScholarGateAdatkészlet ↗ |
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