Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1983 · 1.1K citations · 14 references
Public HealthBiostatisticsCardiologyCausal InferenceEpidemiologic MethodsEpidemiologyBinary Response ModelsCausal Effect EstimationCausal Graphical ModelsCovariate AnalysisCovariate ModelingTreatment EffectsCategorical Covariate EffectsCausal EstimationCausal Sensitivity AnalysisConfounding AdjustmentRobustness To Unmeasured ConfoundingTreatment Assignment ModelingUnobserved ConfoundingUnobserved Heterogeneity
SUMMARY This paper proposes a simple technique for assessing the range of plausible causal conclusions from observational studies with a binary outcome and an observed categorical covariate. The technique assesses the sensitivity of conclusions to assumptions about an unobserved binary covariate relevant to both treatment assignment and response. A medical study of coronary artery disease is used to illustrate the technique.
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