116 citations · 151 across the 6 of their papers we have counts for
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stat.ML2020★ 6 cited
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners
Elias Chaibub Neto
Linear residualization is a common practice for confounding adjustment in machine learning (ML) applications. Recently, causality-aware predictive modeling has been proposed as an…
stat.ML2020★ 1 cited
Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features
Elias Chaibub Neto, Phil Snyder, Solveig K Sieberts +1
In health related machine learning applications, the training data often corresponds to a non-representative sample from the target populations where the learners will be deployed.…