65th ISI World Statistics Congress

65th ISI World Statistics Congress

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Propensity Score Method with Joint Modeling of Mixed-Type Treatment Variables

Author

C
Grace Chiu

Co-author

  • F
    F. Swen Kuh
  • A
    Anton H. Westveld

Conference

65th ISI World Statistics Congress

Format: IPS Abstract - WSC 2025

Keywords: bayesian modeling, causal treatment effect, copulas, observational studies

Abstract

Current propensity score (PS) modeling frameworks for causal inference in observational studies do not readily allow for simultaneous handling of mixed-type treatment variables, such as continuous, discrete, and ordinal variables. We propose an extended rank likelihood method that utilizes copulas to model latent propensity scores for mixed-type treatment variables. We illustrate two versions of our method on real-world datasets and compare their performance against the traditional ("non-latent") PS method.

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