A machine learning compatible method for ordinal propensity score stratification and matching.

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

SUBMITTER: Greene TJ 

PROVIDER: S-EPMC8919399 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature