Comparer des méthodes

	Test du placebo spatial	Analyse de sensibilité pour la causalité
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000s–2010s	1983–2002
Auteur d'origine	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)
Type	Falsification / robustness check	Diagnostic / robustness check
Source fondatrice	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
Alias	geographic placebo test, spatial falsification test, spatial robustness check, geographic spillover test	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity
Apparentées	4	4
Résumé	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.