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| 인과 추론을 위한 위약 검증× | 숨겨진 편향에 대한 민감도 분석 (로젠바움 경계 / E-값)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2010 | 2002 |
| 창시자≠ | Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity) | Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value) |
| 유형≠ | Falsification / robustness test family for causal inference | Sensitivity analysis for causal inference |
| 원전≠ | Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| 별칭≠ | falsification tests, placebo checks, refutation tests, Plasebo Testleri — Nedensel Çıkarım Doğrulama | Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity |
| 관련 | 5 | 5 |
| 요약≠ | Placebo tests are a family of falsification checks that probe the credibility of a causal claim by re-running the analysis on a fake treatment, a false intervention date, or an outcome that should not have been affected. The approach was popularised through the synthetic control work of Abadie, Diamond and Hainmueller (2010) and the regression-discontinuity validity checks of Imbens and Lemieux (2008). | Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017). |
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