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| Kiểm định Giả dược Bayes× | Phương pháp Kiểm soát Tổng hợp Bayes× | |
|---|---|---|
| Lĩnh vực | Suy luận nhân quả | Suy luận nhân quả |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 2010-2015 | 2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal) |
| Người khởi xướng≠ | Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition) | Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller |
| Loại≠ | Robustness check / falsification test | Bayesian causal inference / synthetic control |
| Công trình gốc | 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 ↗ |
| Tên gọi khác | Bayesian falsification test, Bayesian permutation placebo, Bayesian robustness check, Bayesian in-time placebo | Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC |
| Liên quan | 5 | 5 |
| Tóm tắt≠ | 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. | The Bayesian Synthetic Control Method estimates the causal effect of an intervention on a single treated unit by constructing a probabilistic counterfactual from a weighted combination of untreated donor units. Unlike the classical SCM, it places a prior distribution over the synthetic weights, yielding full posterior uncertainty intervals for the counterfactual trajectory and the treatment effect at each post-intervention time point. |
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