“Two potential obstacles stand between the observation of a statistical correlation and the design (and deployment) of an effective intervention, omitted variable bias and reverse causality. Whereas the former has received ample attention, comparably scant focus has been devoted to the latter in the methodological literature. … In this article, we draw upon advances in machine learning, specifically the recently established link between causal direction and the effectiveness of semi-supervised learning algorithms, to develop a novel method for reverse causality testing that circumvents many of the assumptions required by traditional methods.”

Find the paper and full list of authors in Psychometrika.