Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs

Abstract

Unless strong assumptions are made, nonparametric identification of principal causal effects can only be partial and bounds (or sets) for the causal effects are established. In the presence of a secondary outcome, recent results exist to sharpen the bounds that exploit conditional independence assumptions. More general results, though not embedded in a causal framework, can be found in concentration graphical models with a latent variable. The aim of this article is to establish a link between the two settings and to show that adapting and extending results pertaining to concentration graphical models can help achieving identification of principal casual effects in studies when more than one additional outcome is available. Model selection criteria are also suggested. An empirical illustrative example is provided, using data from a real social experiment.