Abstract
Background: In observational research on causal effects, missing data and confounding are very common problems. Multiple imputation and propensity score methods have gained increasing interest as methods to deal with these, but despite their popularity methodologists have mainly focused on how they perform in isolation. Methods: We studied two approaches
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