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, 20 (3), 361-383

Causal Inference in Latent Class Analysis

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Causal Inference in Latent Class Analysis

Stephanie T Lanza et al. Struct Equ Modeling.

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 the present article, two 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.

Keywords: average causal effect; causal inference; latent class analysis; propensity scores.

Figures

Figure 1
Figure 1
Boxplot of the propensity scores for each college enrollment group (for one imputed dataset).
Figure 2
Figure 2
Standardized mean differences between college enrolled and non-enrolled groups on measured confounders before and after propensity score adjustment based on inverse propensity weights applied to full sample (N=1092) for estimating ACE (for one imputed dataset).
Figure 3
Figure 3
Standardized mean differences between college enrolled and non-enrolled groups on measured confounders before and after propensity score adjustment based on inverse propensity weights applied to full sample (N=1092) for estimating ACEC (for one imputed dataset).
Figure 4
Figure 4
Standardized mean differences between college enrolled and non-enrolled groups on measured confounders before and after propensity score adjustment based on matched sample (N=584) for estimating ACEC (for one imputed dataset).

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