用概率模型就能完成因果推断，无需专门工具。
Probabilistic Modelling is Sufficient for Causal Inference
- 所有因果问题都可转化为概率建模与推断
- 通过构建联合概率分布解决因果推理
- 适合想理解因果本质的机器学习研究者
因果推断是机器学习的关键研究方向，但学界对其所需工具存在广泛误解。当前许多文献声称必须使用专门的因果框架或符号才能回答因果问题。本文明确指出：在标准概率建模与推断框架内，无需依赖特定因果工具或符号，即可解答任何因果推断问题。通过具体案例，我们展示如何通过完整写出所有变量的概率分布来处理因果问题。最后，我们将传统因果工具重新解释为标准概率建模自然产生的结果，阐明其必要性与实用性。
原文摘要 · Abstract (English)
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.
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