Сравнение методов

	Пространственный тест-плацебо	Анализ чувствительности для причинно-следственных связей
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2000s–2010s	1983–2002
Автор метода	Developed organically in spatial econometrics and geographic RDD literature; prominent use in Dell (2010) and related work	Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)
Тип	Falsification / robustness check	Diagnostic / robustness check
Основополагающий источник	Buonanno, P., Montolio, D., & Vanin, P. (2009). Does Social Capital Reduce Crime? Journal of Law and Economics, 52(1), 145-170.	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
Другие названия	geographic placebo test, spatial falsification test, spatial robustness check, geographic spillover test	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity
Связанные	4	4
Сводка	A spatial placebo test is a falsification check used in geographic or spatial causal-inference studies. The analyst applies the same estimation procedure to spatial units, boundaries, or zones where no treatment effect should exist — fake borders, shifted cutoffs, or buffer areas beyond spillover range — and checks whether a spurious effect emerges. A non-significant result in the placebo region supports the credibility of the main causal estimate.	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.