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Performance assessment as an application of causal inference

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  • Nicholas T. Longford

Abstract

Institutions in healthcare, education and other public services are under constant pressure to perform to high standards and with efficiency. Assessment of their performance is often problematic because it either ignores important background variables of their patients, students or clients (the casemix), or adjusts for them in a way that is not equitable or transparent. We apply a method of indirect standardization motivated by the potential outcomes framework, in which we consider the hypothetical scenario of an institution's case‐load being dispersed for treatment throughout the domain of assessment (the country's institutions). The target of estimation is the difference of the means of the outcomes in the realized and hypothetical settings. The method is applied to estimating the prevalence of bronchopulmonary dysplasia in extremely preterm‐born infants in the neonatal units and their networks in Great Britain in 2017. The prevalence of bronchopulmonary dysplasia is an audit measure in the UK National Neonatal Audit Programme.

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  • Nicholas T. Longford, 2020. "Performance assessment as an application of causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1363-1385, October.
    • Handle: RePEc:bla:jorssa:v:183:y:2020:i:4:p:1363-1385
    DOI: 10.1111/rssa.12529
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    References listed on IDEAS

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    Cited by:

    1. Marco Doretti & Giorgio E. Montanari, 2024. "Classification and estimation of case-mix adjusted performance indices for binary outcomes," Annals of Operations Research, Springer, vol. 342(3), pages 2201-2225, November.

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