Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Vlerësimi Bajezian i Ndikimit Kunderfaktual× | Analiza e Ndikimit Shkakësor× | |
|---|---|---|
| Fusha | Inferenca kauzale | Inferenca kauzale |
| Familja | Regression model | Regression model |
| Viti i origjinës≠ | 2015 (canonical implementation); Rubin potential outcomes: 1974-2005 | 2015 |
| Krijuesi≠ | Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework | Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google) |
| Lloji≠ | Bayesian causal inference / counterfactual estimation | Bayesian causal inference / counterfactual forecasting |
| Burimi themelues | 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 ↗ |
| Emërtime të tjera | Bayesian CIE, Bayesian causal impact, Bayesian structural time-series causal inference, BSTS counterfactual evaluation | CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis |
| Të lidhura | 5 | 5 |
| Përmbledhja≠ | 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. | 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. |
| ScholarGateSeti i të dhënave ↗ |
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