Regression on principal component or discriminant scores
Dimension reduction techniques reduce the number of candidate explanatory variables. Perhaps best known is the replacement of a large number of candidate explanatory variables by the first few principal components. The hope is that they will adequately summarize the information in the candidate explanatory variables. In favorable circumstances, simple modifications of the components will give new variables that are readily interpretable, but this is not always the case.
Propensity scores, often simply called propensities, may be helpful where a response is compared between two groups ...