Cover: Causal Inference Using Mixture Models

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 Aug 28, 2014

by Michael W. Robbins, Claude Messan Setodji

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.

This report is part of the RAND external publication series. Many RAND studies are published in peer-reviewed scholarly journals, as chapters in commercial books, or as documents published by other organizations.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.