Prediction algorithms are widely used in several domains, including healthcare, yet neither the parameters nor the predictions, have a causal interpretation. A causal interpretation is desirable when using prediction algorithms for decision support to allow for the prediction of the potential outcome of an individual for each intervention under consideration.
With a rich and growing causal inference literature that focuses on estimating the causal effects of hypothetical interventions, firmly grounded in the potential outcomes framework, there is an opportunity to embrace and integrate these methods to allow a predictive algorithm to become meaningful in a causal sense, and thus allow appropriate use of prediction algorithms to guide decisions. With the anticipated increase of (automated) algorithm-based decisions in coming years, following advances in machine learning and artificial intelligence, there is an urgent need for a greater understanding of how causal reasoning can be integrated in predictive analytics.
