Felix J Clouth
Biographic Data
| ID | 1237732 |
|---|---|
| NAME | Felix J Clouth |
| GIVEN NAMES | Felix J |
| FAMILY NAME | Clouth |
| SIGNATURE | CLOUTH F J |
| AFFILIATIONS | Tilburg University |
| ORCID | 0000-0002-8359-9228 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Causal Latent Class Analysis with Distal Outcomes
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.…
Causal Inference for Latent Markov Models Using the Parametric G-Formula
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 cons…
Three-Step Latent Class Analysis with Inverse Propensity Weighting in the Presence of Differential Item Functioning
The integration of causal inference techniques such as inverse propensity weighting (IPW) with latent class analysis (LCA) allows for estimating the effect of a treatment on class membership even with observational data. In this article, we present an extension of the bias-adjusted three-step LCA with IPW, which allows accounting for differential item function (DIF) caused by the treatment or exposure variable. Following the approach by Vermunt a…
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Three-Step Latent Class Analysis with Inverse Propensity Weighting in the Presence of Differential Item Functioning
The integration of causal inference techniques such as inverse propensity weighting (IPW) with latent class analysis (LCA) allows for estimating the effect of a treatment on class membership even with observational data. In this article, we present an extension of the bias-adjusted three-step LCA with IPW, which allows accounting for differential item function (DIF) caused by the treatment or exposure variable. Following the approach by Vermunt a…
Causal Latent Class Analysis with Distal Outcomes
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.…
Causal Inference for Latent Markov Models Using the Parametric G-Formula
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 cons…
Statistical Methods and Inference (3 works) · Advanced Causal Inference Techniques (2 works) · Artificial Intelligence (2 works) · Causal inference (2 works) · Computer Science (2 works) · Econometrics (2 works) · Inference (2 works) · Inverse probability weighting (2 works) · Latent variable (2 works) · Mathematics (2 works)