Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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Identification of principal causal effects using secondary outcomes
Fabrizia Mealli, Barbara Pacini, Elena Stanghellini

Last modified: 2013-06-16


Unless strong assumptions are made, identification of principal causal effects in causal studies 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 Mealli and Pacini, 2012). More general results, though not  embedded in a causal framework, can be found on  concentration graphs with a latent variable (Stanghellini and Vantaggi, 2013). The aim of this paper is to establish a link  between the two settings and to show that adapting results contained in the latter paper can help achieving identification of principal casual effects in studies with more than one secondary outcome.

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