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.
Identification of principal causal effects using secondary outcomes
STANGHELLINI, Elena;
2014
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
