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Analýza citlivosti pro kauzalitu ve výzkumu vzdělávání×Metody párování (CEM / optimální / genetické)×
OborKauzální inferenceKauzální inference
RodinaRegression modelRegression model
Rok vzniku1983–20022012
TvůrcePaul R. Rosenbaum (formal framework); applied in education research by Briggs and othersIacus, King & Porro (CEM); Hansen (optimal/full matching)
TypCausal robustness / bias assessmentMatching for causal inference
Původní zdrojRosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗
Další názvyRosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studiescoarsened exact matching, optimal matching, genetic matching, CEM
Příbuzné65
ShrnutíSensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings.Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching.
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ScholarGatePorovnat metody: Sensitivity analysis for causality in education research · Matching Methods. Získáno 2026-06-18 z https://scholargate.app/cs/compare