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| 베이지안 플라시보 검정× | 인과관계에 대한 민감도 분석× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2010-2015 | 1983–2002 |
| 창시자≠ | Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition) | Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach) |
| 유형≠ | Robustness check / falsification test | Diagnostic / robustness check |
| 원전≠ | 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 ↗ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| 별칭 | Bayesian falsification test, Bayesian permutation placebo, Bayesian robustness check, Bayesian in-time placebo | sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis. |
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