Inference of causal structure using the unobservable
Benoit Desjardins · Journal of Experimental & Theoretical Artificial Intelligence · 2001
Current constraint-based approaches to the discovery of causal structure in statistical data are unable to discriminate between causal models which entail identical sets of marginal dependencies. Often, marginal dependencies between observed variables are the result of complex causal connections involving observed and latent variables. This paper shows that, in such cases, the latent causal structure in a model often entails properties which can be tested against empirical evidence, and thus used to discriminate between equivalent alternative models of an empirical phenomenon under study.