Propensity score matching for multiple treatment levels: A CODA-based contribution
Hajime Seya, Takahiro Yoshida
TL;DR
This paper introduces a straightforward method for propensity score matching across multiple treatment levels using Aitchison distance from compositional data analysis, under the assumption of strong unconfoundedness.
Contribution
It presents a novel application of CODA techniques, specifically Aitchison distance, to improve propensity score matching for multiple treatments.
Findings
Effective matching across multiple treatment groups
Improved balance in covariates after matching
Potential for broader application in causal inference
Abstract
This study proposes a simple technique for propensity score matching for multiple treatment levels under the strong unconfoundedness assumption with the help of the Aitchison distance proposed in the field of compositional data analysis (CODA).
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Taxonomy
TopicsGeochemistry and Geologic Mapping · Statistical Methods and Inference