Automatic Generation of Plausible Co-Occurring Causes for Effects Explanation or Prediction

In numerous contexts, ranging from systems safety assessment to finance and medical diagnosis, a relevant causal inference task is to predict unseen rare events—the so-called black swans . These are plausible, high-impact, but unexpected events for whose prediction a probabilistic-based causal inference falls short. For instance, a safety analyst needs to hypothesize potential rare co-causes that could lead to an accident, so as to manage the most unexpected failures besides the more obvious ones. Given an effect, we use abduction to support the generation of a plausible set of explanatory hypotheses for its causes. We present a generative evolutionary strategy—called Evolutionary Abduction (EVA)—for automating abductive inference by repeatedly constructing hypothetical cause-effect instances, and then automatically assessing their plausibility as well as their novelty with respect to already known instances—a mechanism mimicking the human reasoning employed whenever we need to select the best candidates from a set of hypotheses. Experiments with four datasets confirm that EVA can construct new and realistic multiple-cause hypotheses for a given effect. EVA outperforms alternative strategies based on probabilistic-based causal inference as well as state-of-the-art evolutionary algorithms, generating closer-to-real instances in most settings and datasets.