Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs

Unless strong assumptions are made, nonparametric identification of principal causal effects can only be partial. 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. The aim of this article is to establish a link between the two settings and to show that adapting results pertaining to concentration graphical models can help achieving identification os principal causal effects in studeis when more than one additional outcome is available.