Сравнение методов

	Робастный анализ причинно-следственного воздействия	Анализ чувствительности для причинно-следственных связей
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2015	1983–2002
Автор метода	Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)	Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)
Тип	Bayesian causal inference with robustness validation	Diagnostic / robustness check
Основополагающий источник	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.	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
Другие названия	robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity
Связанные	5	4
Сводка	Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity analysis, and prior sensitivity assessments — to verify that a detected intervention effect is genuine and not an artifact of model choices or coincidental data patterns.	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.