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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Causal Inference Through Principal Stratification: A Special Type of Latent Class Modelling
verfasst von : Leonardo Grilli
Erschienen in: Classification and Multivariate Analysis for Complex Data Structures
Verlag: Springer Berlin Heidelberg
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Principal stratification is an increasingly adopted framework for drawing counterfactual causal inferences in complex situations. After outlining the framework, with special emphasis on the case of truncation by death, I describe an application of the methodology where the analysis is based on a parametric model with latent classes. Then, I discuss the special features of latent class models derived within the principal strata framework. I argue that the concept of principal stratification gives latent class models a solid theoretical basis and helps to solve some specification and fitting issues.