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Found 4 papers, 2 papers with code
Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection
When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model performance estimates on the observed population may be misleading.
When black box algorithms are (not) appropriate: a principled prediction-problem ontology
In this paper, we introduce the term "outcome reasoning" to refer to this form of reasoning.
Other Statistics
Using the Prognostic Score to Reduce Heterogeneity in Observational Studies
In large sample observational studies, the control population often greatly outnumbers the treatment population.
Methodology
A comparison of methods for model selection when estimating individual treatment effects
Instead of relying on a single method, multiple models fit by a diverse set of algorithms should be evaluated against each other using an objective function learned from the validation set.
