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Joint modelling of multiple treatment variables for a single outcome: A Bayesian approach

Kuh, Swen; Westveld, Anton; Chiu, Grace S.


Current frameworks for causal inference in observational studies do not readily allow for the joint modelling of different types of treatment variables, such as a mix of continuous and discrete data. In this work, we propose an extended rank likelihood method [Hoff (2007)] for the inference of two latent parameterisations of the propensity score; the latent nature of the score is due to the copula framework. This allows for the simultaneous inclusion of different types of treatment variables...[Show more]

CollectionsANU Research Publications
Date published: 2024
Type: Poster
Access Rights: Open Access


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