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Linear residualization is a common practice for confounding adjustment inmachine learning (ML) applications. Recently, causality-aware predictivemodeling has been proposed as an alternative causality-inspired approach foradjusting for confounders. The basic idea is to simulate counterfactual datathat is free from the spurious associations generated by the observedconfounders. In this paper, we compare the linear residualization approachagainst the causality-aware confounding adjustment in anticausal predictiontasks, and show that the causality-aware approach tends to (asymptotically)outperform the residualization adjustment in terms of predictive performance inlinear learners. Importantly, our results still holds even when the true modelis not linear. We illustrate our results in both regression and classificationtasks, where we compared the causality-aware and residualization approachesusing mean squared errors and classification accuracy in synthetic dataexperiments where the linear regression model is mispecified, as well as, whenthe linear model is correctly specified. Furthermore, we illustrate how thecausality-aware approach is more stable than residualization with respect todataset shifts in the joint distribution of the confounders and outcomevariables.

Causality-aware counterfactual confounding adjustment as an alternative  to linear residualization in anticausal prediction tasks based on linear  learners