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 language | English (US) |
|---|---|
| Pages (from-to) | 361-383 |
| Number of pages | 23 |
| Journal | Structural Equation Modeling |
| Volume | 20 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 1 2013 |
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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)
Cite this
Causal Inference in Latent Class Analysis. / Lanza, Stephanie T.; Coffman, Donna L.; Xu, Violet Shu.
In: Structural Equation Modeling, Vol. 20, No. 3, 01.07.2013, p. 361-383.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Causal Inference in Latent Class Analysis
AU - Lanza, Stephanie T.
AU - Coffman, Donna L.
AU - Xu, Violet Shu
PY - 2013/7/1
Y1 - 2013/7/1
N2 - 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.
AB - 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.
KW - average causal effect
KW - causal inference
KW - latent class analysis
KW - propensity scores
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U2 - 10.1080/10705511.2013.797816
DO - 10.1080/10705511.2013.797816
M3 - Article
VL - 20
SP - 361
EP - 383
JO - Structural Equation Modeling
JF - Structural Equation Modeling
SN - 1070-5511
IS - 3
ER -