Abstract
Big data and machine learning techniques offer opportunities to investigate the effects of psychological factors on pain outcomes. Nevertheless, these advances can only deliver when the quality of the data is high and the underpinning causal assumptions are considered. We argue that there is room for improvement and identify some challenges in the evidence base concerning the effect of psychological factors on the development and maintenance of chronic pain. As a starting point, 3 basic tenets of causality are taken: (1) cause and effect differ from each other, (2) the cause precedes the effect within reasonable time, and (3) alternative explanations are ruled out. Building on these tenets, potential problems and some lessons learned are provided that the next generation of research should take into account. In particular, there is a need to be more explicit and transparent about causal assumptions in research. This will lead to better research designs, more appropriate statistical analyses, and constructive discussions and productive tensions that improve our science.