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| 稳健因果影响分析× | 因果影响分析× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份 | 2015 | 2015 |
| 提出者≠ | Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework) | Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google) |
| 类型≠ | Bayesian causal inference with robustness validation | Bayesian causal inference / counterfactual forecasting |
| 开创性文献 | 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 ↗ |
| 别名 | robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference | CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | 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. |
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