Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature that you need a bespoke causal framework or notation to answer causal questions. In this paper, we want to make it clear that you \emph{can} answer any causal inference question within the realm of probabilistic modelling and inference, without causal-specific tools or notation. Through concrete examples, we demonstrate how causal questions can be tackled by writing down the probability of everything. Lastly, we reinterpret causal tools as emerging from standard probabilistic modelling and inference, elucidating their necessity and utility.

翻译：因果推断是机器学习的关键研究领域，然而关于解决该问题所需工具的认识仍存在混淆。机器学习文献中普遍存在一种观点，认为需要专门的因果框架或符号体系才能回答因果问题。本文旨在阐明，在概率建模与推断的范畴内，\emph{完全能够}在不借助因果专用工具或符号的情况下回答任何因果推断问题。通过具体案例，我们展示了如何通过"为所有变量建立概率模型"的方式处理因果问题。最后，我们将因果工具重新阐释为标准概率建模与推断的衍生成果，从而阐明其必要性与实用性。