The language of information theory is favored in both causal reasoning and machine learning frameworks. But, is there a better language than this? In this study, we demonstrate the pitfalls of infotheoretic estimation using first order statistics on (short) sequences for causal learning. We recommend the use of data compression based approaches for causality testing since these make very little assumptions on data as opposed to infotheoretic measures, and are more robust to finite data length effects. We conclude with a discussion on the challenges posed in modeling the effects of conditioning process $X$ with another process $Y$ in causal machine learning. Specifically, conditioning can increase 'confusion' which can be difficult to model by classical information theory. A conscious causal agent creates new choices, decisions and meaning which poses huge challenges for AI.

翻译：信息理论的语言在因果推理和机器学习框架中都得到偏好。 但是,是否有比这更好的语言呢?在本研究中,我们展示了使用关于因果学习顺序的第一顺序统计进行信息理论估算的陷阱。我们建议使用基于数据压缩的方法进行因果测试,因为这些方法对数据没有多少假设,而不是信息理论措施,并且对有限的数据长度效果更强有力。我们最后讨论在模拟因果机学中使用另一个过程($X$)的影响方面带来的挑战。具体地说,由于典型的信息理论很难模拟,调整可以增加“混杂 ” 。 一种有意识的因果因素创造了新的选择、决定和意义,给AI带来巨大的挑战。