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
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.