Causal Inference for Latent Markov Models Using the Parametric G-Formula
Datos Bibliográficos
| ID | 3591103 |
|---|---|
| Autores | Felix J Clouth (0000-0002-8359-9228, Tilburg University, autor de correspondencia), Maarten J Bijlsma (0000-0002-7330-6006, Pharmacotherapy, Epidemiology and Economics, University of Groningen, Groningen, The Netherlands), Steffen Pauw (0000-0003-2257-9239, Tilburg University), Jeroen K Vermunt (0000-0001-9053-9330, Tilburg University) |
| Año | 2025 |
| Fecha de publicación | 2025-09-26 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Sociological Methods & Research (JOURNAL) |
| Identificadores de la revista | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Editorial | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/00491241251377068 |
| OpenAlex | W4414535229 |
| Idioma | EN |
| Referencias citadas | 50 |
The parametric g-formula can be used to estimate causal effects of time-varying exposures on observable outcomes. It resolves intermediate confounding in such settings by specifying several parametric models, one each for every time-varying variable, and by performing micro-simulations. However, its restriction to applications with observable outcomes limits its usability for social sciences where variables of interest are often unobservable constructs. In such cases, measurement models are needed. We propose a new approach utilizing bias-adjusted three-step latent Markov models (LMMs) within the parametric g-formula. LMMs estimate the probability of membership in an unobservable state conditional on observed indicator variables. By replacing the parametric models in the g-formula with LMMs, micro-simulations are performed as usual to estimate a causal effect of the time-varying exposure. We illustrate this new approach by estimating the average treatment effect of unemployment on several unobservable mental health states utilizing longitudinal data from the Longitudinal Internet studies for the Social Sciences panel
Causal inference · Causal model · Conditional probability · Covariate · Inference · Latent variable · Parametric model · Parametric statistics · Unobservable · Advanced Causal Inference Techniques · Bayesian Modeling and Causal Inference · Statistical Methods and Inference
Principled missing data methods for researchers
The role of the propensity score in estimating dose-response functions
A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
Exploratory latent structure analysis using both identifiable and unidentifiable models
The increasing burden of depression
Latent Variables in Psychology and the Social Sciences
Mental health affects future employment as job loss affects mental health
The central role of the propensity score in observational studies for causal effects
A general approach to causal mediation analysis.
Stepwise Latent Class Analysis in the Presence of Missing Values on the Class Indicators
How to Perform Three-Step Latent Class Analysis in the Presence of Measurement Non-Invariance or Differential Item Functioning
Three-Step Latent Class Analysis with Inverse Propensity Weighting in the Presence of Differential Item Functioning
Unreliable Continuous Treatment Indicators in Propensity Score Analysis
The reciprocal relationship between depressive symptoms and employment status
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
Making the Most of Statistical Analyses
Recent Developments in the Econometrics of Program Evaluation
Latent Structure Analysis
Unemployment and subsequent depression
Associations between unemployment and major depressive disorder
Analysis of Cross-Cultural Comparability of Pisa 2009 Scores
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |