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Porovnat metody

Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.

Analýza citlivosti heterogenních účinků léčby pro kauzalitu×Rozdíl v rozdílech (Diff-in-Diff)×
OborKauzální inferenceEkonometrie
RodinaRegression modelRegression model
Rok vzniku2000s–2010s1994
TvůrceRosenbaum (sensitivity analysis framework); extended to heterogeneous effects by Crump, Imbens, and othersCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TypRobustness / sensitivity checkCausal inference / panel regression
Původní zdrojRosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Další názvyHTE sensitivity analysis, heterogeneous-effects sensitivity analysis, sensitivity analysis with effect heterogeneity, HTE robustness analysisdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Příbuzné55
ShrnutíHeterogeneous 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.
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ScholarGatePorovnat metody: Heterogeneous Treatment Effect Sensitivity Analysis for Causality · Difference-in-Differences. Získáno 2026-06-17 z https://scholargate.app/cs/compare