Causal Effect of Count Treatment on Ordinal Outcome Using Generalized Propensity Score: Application to Number of Antenatal Care and Age Specific Childhood Vaccination.

ABSTRACT:

Background

Many of the studies in causal inference using propensity scores relied on binary treatments where it is estimated by logistic regression or machine learning algorithms. Since 2000s, attention has been given for multiple values (categorical) and continuous treatments and the propensity score associated with such treatments is called generalized propensity score (GPS). However, there is scant literature on the use of count treatments in causal inference. Besides, effective sample size, after weighting, along with other methods has not been practiced for GPS model performance measure. The study was done with the aim of using count treatments in causal inference; select appropriate GPS and outcome models for such treatment and ordinal outcome.

Method

A family of count

SUBMITTER: Iyassu AS 

PROVIDER: S-EPMC11822178 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature