Causal Deep Learning
Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential remains largely unlocked as causality requires some crucial assumptions which cannot be tested in practice. To address this challenge and make progress in solving real-world problems, we propose a new way of thinking about causality – we call this causal deep learning. The framework we propose for causal deep learning spans three dimensions: (1) a parametric dimension; (2) a structural dimension, which allows incomplete (testable) causal knowledge rather than assuming either full or no causal knowledge; and finally, (3) a temporal dimension, which explicitly allows for situations which capture exposure times or temporal structure. Together, these dimensions allow us to make progress on a variety of real-world problems by leveraging (sometimes incomplete) causal knowledge and/or combining diverse causal deep learning methods.