Comparer des méthodes

	Analyse de sensibilité des effets hétérogènes du traitement pour la causalité	Différence-en-différences (Diff-in-Diff)
Domaine	Inférence causale	Économétrie
Famille	Regression model	Regression model
Année d'origine	2000s–2010s	1994
Auteur d'origine	Rosenbaum (sensitivity analysis framework); extended to heterogeneous effects by Crump, Imbens, and others	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Robustness / sensitivity check	Causal inference / panel regression
Source fondatrice	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	HTE sensitivity analysis, heterogeneous-effects sensitivity analysis, sensitivity analysis with effect heterogeneity, HTE robustness analysis	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Apparentées	5	5
Résumé	Heterogeneous Treatment Effect Sensitivity Analysis examines how robust subgroup-specific causal estimates are to unobserved confounding. Rather than testing a single average treatment effect, it asks whether the estimated variation in treatment effects across units or subgroups could be explained away by hidden bias, and at what level of hidden bias the causal conclusions for each subgroup would break down.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.