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 As being the treatment only controls for observed variables, any hidden bias because of latent variables may perhaps continue being just after matching.[four] One more issue is usually that PSM involves substantial samples, with substantial overlap amongst treatment and Regulate groups.
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If units within the procedure and Command were balanced on a lot of covariates one by one, large quantities of observations might be needed to overcome the "dimensionality challenge" whereby the introduction of a completely new balancing covariate improves the least required number of observations during the sample geometrically.
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Suppose that we have a binary cure T, an consequence Y, and background variables X. The propensity score is described since the conditional chance of procedure given background variables:
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The potential of bias arises because the obvious big difference in end result concerning these two teams of units may perhaps depend upon qualities that afflicted if a unit been given a specified treatment method in lieu of a result of the impact of your remedy for every se. In randomized experiments, the randomization permits unbiased estimation of cure effects; for each covariate, randomization implies that procedure-teams will probably be balanced on common, via the law of large figures.
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