论文标题
使用可拖动电路的因果推断
Causal Inference Using Tractable Circuits
论文作者
论文摘要
本文的目的是讨论最近的结果，该结果表明，在（未知）因果机制存在下的概率推论对于传统上被视为棘手的模型可能是可探讨的。该结果最近报告是为了促进基于模型的监督学习，但可以在因果关系的情况下解释，如下所示。一个人可以将非参数因果图编译成一个算术电路，该电路支持电路大小的时间线性推断。该电路也是非参数，因此可以使用数据来估算数据的参数，并在这些估计值参数的因果图中进行进一步的原因（以线性时间）进行估算。此外，即使不是因果图的树宽，电路尺寸有时也可以界定，从而导致对先前被认为是棘手的模型的可行推断。这是通过一种可以在计算上利用因果机制的新技术来实现的，但不需要知道其身份（因果推理中的经典设置）。我们的目标是为这些新结果提供以因果关系为导向的暴露，并推测它们可能有可能促进更可扩展和多才多艺的因果推论。
The aim of this paper is to discuss a recent result which shows that probabilistic inference in the presence of (unknown) causal mechanisms can be tractable for models that have traditionally been viewed as intractable. This result was reported recently to facilitate model-based supervised learning but it can be interpreted in a causality context as follows. One can compile a non-parametric causal graph into an arithmetic circuit that supports inference in time linear in the circuit size. The circuit is also non-parametric so it can be used to estimate parameters from data and to further reason (in linear time) about the causal graph parametrized by these estimates. Moreover, the circuit size can sometimes be bounded even when the treewidth of the causal graph is not, leading to tractable inference on models that have been deemed intractable previously. This has been enabled by a new technique that can exploit causal mechanisms computationally but without needing to know their identities (the classical setup in causal inference). Our goal is to provide a causality-oriented exposure to these new results and to speculate on how they may potentially contribute to more scalable and versatile causal inference.
