Causal Inference in Latent Class Analysis

Stephanie T. Lanza, Donna L. Coffman, Shu Xu

Research output: Contribution to journalArticle

Abstract

The integration of modern methods for causal inference with latent class analysis (LCA) allows social, behavioral, and health researchers to address important questions about the determinants of latent class membership. In this article, 2 propensity score techniques, matching and inverse propensity weighting, are demonstrated for conducting causal inference in LCA. The different causal questions that can be addressed with these techniques are carefully delineated. An empirical analysis based on data from the National Longitudinal Survey of Youth 1979 is presented, where college enrollment is examined as the exposure (i.e., treatment) variable and its causal effect on adult substance use latent class membership is estimated. A step-by-step procedure for conducting causal inference in LCA, including multiple imputation of missing data on the confounders, exposure variable, and multivariate outcome, is included. Sample syntax for carrying out the analysis using SAS and R is given in an appendix.

Original languageEnglish (US)
Pages (from-to)361-383
Number of pages23
JournalStructural Equation Modeling
Volume20
Issue number3
DOIs
StatePublished - Jul 2013

Keywords

  • average causal effect
  • causal inference
  • latent class analysis
  • propensity scores

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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