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Metode slaganja (CEM / Optimalno / Genetičko)×Sensitivity Analysis for Unmeasured Confounding×
OblastKauzalno zaključivanjeKauzalno zaključivanje
PorodicaRegression modelRegression model
Godina nastanka20122002
TvoracIacus, King & Porro (CEM); Hansen (optimal/full matching)Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)
TipMatching for causal inferenceSensitivity analysis for causal inference
Temeljni izvorIacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
Drugi nazivicoarsened exact matching, optimal matching, genetic matching, CEMRosenbaum bounds, E-value, hidden bias sensitivity analysis, unmeasured confounding sensitivity
Srodne55
SažetakMatching 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.Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017).
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ScholarGateUporedite metode: Matching Methods · Sensitivity Analysis for Unmeasured Confounding. Preuzeto 2026-06-15 sa https://scholargate.app/sr/compare