A Machine Learning Compatible Method For Ordinal Propensity Score Stratification and Matching

Authors

Thomas J Greene
Stacia M DeSantis
Derek W Brown
Anna V Wilkinson
Michael D Swartz

Publication Date

3-15-2021

Journal

Statistics in Medicine

Abstract

Although machine learning techniques that estimate propensity scores for observational studies with multivalued treatments have advanced rapidly in recent years, the development of propensity score adjustment techniques has not kept pace. While machine learning propensity models provide numerous benefits, they do not produce a single variable balancing score that can be used for propensity score stratification and matching. This issue motivates the development of a flexible ordinal propensity scoring methodology that does not require parametric assumptions for the propensity model. The proposed method fits a one-parameter power function to the cumulative distribution function (CDF) of the generalized propensity score (GPS) vector resulting from any machine learning propensity model, and is henceforth called the GPS-CDF method. The estimated parameter from the GPS-CDF method,

Keywords

Adolescent, Causality, Child, Computer Simulation, Humans, Machine Learning, Propensity Score, Research Design

DOI

10.1002/sim.8846

PMID

33352615

PMCID

PMC8919399

PubMedCentral® Posted Date

March 2022

PubMedCentral® Full Text Version

Author MSS