Propensity Score Matching - Statpower

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IntroductionModeling the CovariatesSubclassificationMatchingBalancing ScoresThe Propensity ScoreMatching MethodsUsing Propensity Scores – A General StrategyAn ExampleDifficulties with SubclassificationSubclassification has advantages:It forces overlapIt imposes roughly the same covariate distribution withinsubclassesHowever, it has disadvantages as well:When categorizing a continuous covariate, someinformation will be lostThe strategy is very difficult to implement with severalcovariates at onceMultilevelPropensity Score Matching
IntroductionModeling the CovariatesSubclassificationMatchingBalancing ScoresThe Propensity ScoreMatching MethodsUsing Propensity Scores – A General StrategyAn ExampleMatchingIntroductionWhy Match?Matching refers to a variety of procedures that restrict andreorganize the original sample in preparation for a statisticalanalysis. The simplest version is to do one-to-one matching,with each treatment observation being paired with a controlthat is as much like it as possible on the relevant covariates.With multiple covariates, matching can be done on a “nearestneighbor” basis. For example, a treatment observation ismatched according to the minimum Mahalanobis distance,which is, for two vectors of scores x (1) and x (2) on thecovariates,(x (1) − x (2) ) ′ Σ −1 (x (1) − x (2) ) (2)MultilevelPropensity Score Matching
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Propensity Score Matching - Statpower

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