Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Test placebo sur données de panel× | Analyse de sensibilité pour la causalité× | |
|---|---|---|
| Domaine | Inférence causale | Inférence causale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2004-2010 | 1983–2002 |
| Auteur d'origine≠ | Bertrand, Duflo & Mullainathan; Abadie, Diamond & Hainmueller | Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach) |
| Type≠ | Falsification / validation test | Diagnostic / robustness check |
| Source fondatrice≠ | Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1), 249-275. DOI ↗ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 |
| Alias | placebo regression test, falsification test, pseudo-treatment test, in-time placebo | sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity |
| Apparentées | 4 | 4 |
| Résumé≠ | A panel data placebo test is a falsification procedure used to assess the credibility of causal estimates in quasi-experimental panel designs. By applying the same estimation strategy to a period, group, or outcome where no true effect should exist, researchers verify that the observed treatment effect is not merely an artifact of model specification, coincidental trends, or data patterns unrelated to the intervention. | 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. |
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