Missing data from a causal perspective

Abstract

This paper applies graph based causal inference procedures for recovering information from missing data. We establish conditions that permit and prohibit recoverability. In the event of theoretical impediments to recoverability, we develop graph based procedures using auxiliary variables and external data to overcome such impediments. We demonstrate the perils of model-blind recovery procedures both in determining whether or not a query is recoverable and in choosing an estimation procedure when recoverability holds.