LCA with covariates models the association between classes and predictors, but causation cannot be inferred unless people are randomly assigned to levels of the predictors of latent class membership. Modern causal inference methods, such as inverse propensity weighting, can be used to adjust for potential confounding in observational data. The Methodology Center pioneered work on applying inverse propensity weights to estimate the causal effects of covariates on latent class membership and to estimate the causal effects of latent class membership on a distal outcome.
More information is coming soon, including code to conduct latent class analysis with inverse propensity score weights! Stay tuned and sign up for our listserv below.