Vertaile menetelmiä

	Heterogeenisten hoitovaikutusten lumetesti	Herkkyysanalyysi kausaalisuudelle
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	2000s–2010s	1983–2002
Kehittäjä	Rosenbaum (placebo test concept); Athey & Imbens (HTE estimation framework)	Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)
Tyyppi	Validation / falsification test	Diagnostic / robustness check
Alkuperäislähde	Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. ISBN: 978-0521885881	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
Rinnakkaisnimet	HTE placebo test, heterogeneous-effect placebo check, subgroup placebo test, CATE placebo validation	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity
Liittyvät	3	4
Tiivistelmä	A placebo test for heterogeneous treatment effects is a falsification strategy used to validate whether estimated variation in treatment effects across subgroups or covariate values is genuine rather than an artifact of model specification, overfitting, or coincidental patterns. By applying the same estimation procedure to pseudo-treatments, fake outcomes, or subgroups that logically should not differ, researchers check that observed heterogeneity reflects real causal variation.	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.