Causality-based tests to detect the influence of confounders on mobile health diagnostic applications: a comparison with restricted permutations

Machine learning practice is often impacted by confounders. Confounding can be particularly severe in remote digital health studies where the participants self-select to enter the study. While many different confounding adjustment approaches have been proposed in the literature, most of these methods rely on modeling assumptions, and it is unclear how robust they are to violations of these assumptions. This realization has recently motivated the development of restricted permutation methods to quantify the influence of observed confounders on the predictive performance of a machine learning models and evaluate if confounding adjustment methods are working as expected. In this paper we show, nonetheless, that restricted permutations can generate biased estimates of the contribution of the confounders to the predictive performance of a learner, and we propose an alternative approach to tackle this problem. By viewing a classification task from a causality perspective, we are able to leverage conditional independence tests between predictions and test set labels and confounders in order to detect confounding on the predictive performance of a classifier. We illustrate the application of our causality-based approach to data collected from mHealth study in Parkinson's disease.