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 since most work so far requires strict assumptions which do not hold
true 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 which we propose for causal deep
learning spans three dimensions: (1) a structural dimension, which allows
incomplete causal knowledge rather than assuming either full or no causal
knowledge; (2) a parametric dimension, which encompasses parametric forms which
are typically ignored; 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. This new framework also enables
researchers to compare systematically across existing works as well as identify
promising research areas which can lead to real-world impact.

提出了一种新的因果关系框架 —— 因果深度学习，这个框架包括三个维度：结构维度、参数维度和时间维度。该框架可以在利用不完整的因果知识的同时，结合多种因果深度学习方法，解决各种实际问题，从而实现真正的实际影响。同时，这个框架还可以让研究人员系统地对现有工作进行比较，并找出有前途的研究领域。