Causal Latent Class Analysis with Distal Outcomes
A Modified Three-Step Method Using Inverse Propensity Weighting
Bibliographic Data
| ID | 19290824 |
|---|---|
| Authors | Trà T Lê (0000-0002-7700-8946, Tilburg University), Felix J Clouth (0000-0002-8359-9228, Tilburg University, corresponding author), Jeroen K Vermunt (0000-0001-9053-9330, Tilburg University) |
| Year | 2025 |
| Volume | 60 |
| Issue | 1 |
| Pages | 30-60 |
| Publication date | 2025-01-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2024.2367485 |
| PMID | 39034808 |
| OpenAlex | W4400894042 |
| Language | EN |
| References cited | 29 |
Bias-adjusted three-step latent class (LC) analysis is a popular technique for estimating the relationship between LC membership and distal outcomes. Since it is impossible to randomize LC membership, causal inference techniques are needed to estimate causal effects leveraging observational data. This paper proposes two novel strategies that make use of propensity scores to estimate the causal effect of LC membership on a distal outcome variable. Both strategies modify the bias-adjusted three-step approach by using propensity scores in the last step to control for confounding. The first strategy utilizes inverse propensity weighting (IPW), whereas the second strategy includes the propensity scores as control variables. Classification errors are accounted for using the BCH or ML corrections. We evaluate the performance of these methods in a simulation study by comparing it with three existing approaches that also use propensity scores in a stepwise LC analysis. Both of our newly proposed methods return essentially unbiased parameter estimates outperforming previously proposed methods. However, for smaller sample sizes our IPW based approach shows large variability in the estimates and can be prone to non-convergence. Furthermore, the use of these newly proposed methods is illustrated using data from the LISS panel
Causal inference · Confounding · Econometrics · Inference · Inverse probability weighting · Latent variable · Observational study · Propensity score matching · Statistics · Weighting · Advanced Causal Inference Techniques · Artificial Intelligence · Computer Science · Mathematics · Medicine · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
Latent structure analysis
Marginal Structural Models and Causal Inference in Epidemiology
Exploratory latent structure analysis using both identifiable and unidentifiable models
The increasing burden of depression
A tutorial on propensity score estimation for multiple treatments using generalized boosted models
Constructing Inverse Probability Weights for Marginal Structural Models
Estimation of Regression Coefficients When Some Regressors are not Always Observed
The central role of the propensity score in observational studies for causal effects
Mice
Nonparametric Estimation of Average Treatment Effects Under Exogeneity
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
The reciprocal relationship between depressive symptoms and employment status
Comorbidity patterns, family history and breast cancer risk
Estimating causal effects from epidemiological data
Estimating the Association between Latent Class Membership and External Variables Using Bias-adjusted Three-step Approaches
Latent Class Modeling with Covariates
Estimating Latent Structure Models with Categorical Variables
Multinomial Logit Latent-Class Regression Models
| Highly cited | No |
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