Methods for Using Selection on Observed Variables to Address Selection on Unobserved Variables
In this paper we develop new estimation methods for identifying causal effects based on the idea that the amount of selection on the observed explanatory variables in a model provides a guide to the amount of selection on the unobservables. Our approach involves the use of factor model as a way to infer properties of unobserved covariates from the observed covariates. We propose a confidence interval estimator that covers the true value of the causal effect.