ArticleStatistics in medicine2021
A machine learning compatible method for ordinal propensity score stratification and matching.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative perioperative outcomes of robot-assisted versus video-assisted thoracoscopic mediastinal tumor resection: a propensity score-matched analysis.Journal of cardiothoracic surgery · 2026Article
- Propensity score stratification methods for continuous treatments.Statistics in medicine · 2021Article
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Authors and funding
5 authors.
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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,
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