A Survey of Methods, Challenges and Perspectives in Causality
This is an incremental survey paper for researchers in AI/ML and causality, summarizing existing work rather than presenting new methods.
This survey paper examines the integration of Deep Learning with Causality to address generalization issues beyond initial data distributions, highlighting early attempts and future perspectives for combining these fields.
Deep Learning models have shown success in a large variety of tasks by extracting correlation patterns from high-dimensional data but still struggle when generalizing out of their initial distribution. As causal engines aim to learn mechanisms independent from a data distribution, combining Deep Learning with Causality can have a great impact on the two fields. In this paper, we further motivate this assumption. We perform an extensive overview of the theories and methods for Causality from different perspectives, with an emphasis on Deep Learning and the challenges met by the two domains. We show early attempts to bring the fields together and the possible perspectives for the future. We finish by providing a large variety of applications for techniques from Causality.