Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Vankka kausaalivaikutusanalyysi× | Bayesiläinen kausaalivaikutusanalyysi× | |
|---|---|---|
| Tieteenala | Kausaalipäättely | Kausaalipäättely |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi | 2015 | 2015 |
| Kehittäjä≠ | Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework) | Brodersen, Gallusser, Koehler, Remy & Scott (Google) |
| Tyyppi≠ | Bayesian causal inference with robustness validation | Bayesian causal inference / time series |
| Alkuperäislähde | 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 ↗ |
| Rinnakkaisnimet | robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference | CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity analysis, and prior sensitivity assessments — to verify that a detected intervention effect is genuine and not an artifact of model choices or coincidental data patterns. | 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. |
| ScholarGateAineisto ↗ |
|
|