Causal Inference Using Mixture Models

A Word of Caution

Published In: Medical Care, v. 52, no. 9, Commentary, Sep. 2014, p. 770-772

Posted on RAND.org on August 28, 2014

by Michael W. Robbins, Claude Messan Setodji

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Mixture models are useful for monitoring the behavior of data and for offering comparisons to supplemental data, especially in the presence of unobserved heterogeneity, but one should be highly cautious when drawing causal inferences as to which population each component of the fitted mixture model represents.

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