  Propensity score methods are common for estimating a binary treatment effect
when treatment assignment is not randomized. When exposure is measured on an
ordinal scale (i.e., low - medium - high), however, propensity score inference
requires extensions which have received limited attention. Estimands of
possible interest with an ordinal exposure are the average treatment effects
between each pair of exposure levels. Using these estimands, it is possible to
determine an optimal exposure level. Traditional methods, including
dichotomization of the exposure or a series of binary propensity score
comparisons across exposure pairs, are generally inadequate for identification
of optimal levels.We combine subclassification with regression adjustment to
estimate transitive, unbiased average causal effects across an ordered
exposure, and apply our method on the 2005-06 National Health and Nutrition
Examination Survey to estimate the effects of nutritional label use on body
mass index.
