Propensity Score Matching - Statpower
Propensity Score Matching - Statpower
Propensity Score Matching - Statpower
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IntroductionModeling the CovariatesSubclassification<strong>Matching</strong>Balancing <strong>Score</strong>sThe <strong>Propensity</strong> <strong>Score</strong><strong>Matching</strong> MethodsUsing <strong>Propensity</strong> <strong>Score</strong>s – A General StrategyAn ExampleThe Strong Ignorability AssumptionDefinition of a <strong>Propensity</strong> <strong>Score</strong>Key AssumptionMathematical PropertiesKey ImplicationsKey QuestionsIn deriving the key optimalit property of propensity scoresRosenbaum and Rubin (1983) assume strong ignorability of Tgiven X .Strong IgnorabilityThe property of strong ignorability of T given X holds if, forpotential outcomes y 1 and y 0 , the distribution of these potentialoutcomes is conditionally independent of T given X , and forany value of the covariates, there is a possibility of a unitreceiving the treatment or not receiving the treatment. That is,(y 1 , y 0 )⊥T |X (5)and0 < Pr(T = 1|X = x) < 1 ∀x (6)Multilevel<strong>Propensity</strong> <strong>Score</strong> <strong>Matching</strong>
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