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We propose a counterfactual approach to train ``causality-aware" predictivemodels that are able to leverage causal information in static anticausalmachine learning tasks (i.e., prediction tasks where the outcome influences thefeatures). In applications plagued by confounding, the approach can be used togenerate predictions that are free from the influence of observed confounders.In applications involving observed mediators, the approach can be used togenerate predictions that only capture the direct or the indirect causalinfluences. Mechanistically, we train supervised learners on (counterfactually)simulated features which retain only the associations generated by the causalrelations of interest. We focus on linear models, where analytical resultsconnecting covariances, causal effects, and prediction mean squared errors arereadily available. Quite importantly, we show that our approach does notrequire knowledge of the full causal graph. It suffices to know which variablesrepresent potential confounders and/or mediators. We discuss the stability ofthe method with respect to dataset shifts generated by selection biases andvalidate the approach using synthetic data experiments.

Towards causality-aware predictions in static anticausal machine learning tasks: the linear structural causal model case