Salīdzināt metodes

	Placebo testi cēloņu noteikšanai	Jutīguma analīze slēptai neobjektivitātei (Rozenbauma robežas / E-vērtība)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2010	2002
Autors	Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity)	Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)
Tips	Falsification / robustness test family for causal inference	Sensitivity analysis for causal inference
Pirmavots	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.	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
Citi nosaukumi	falsification tests, placebo checks, refutation tests, Plasebo Testleri — Nedensel Çıkarım Doğrulama	Rosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity
Saistītās	5	5
Kopsavilkums	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).