论文标题
I-Spec：学习可运输，稳定模型的端到端框架
I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models
论文作者
论文摘要
开发与部署之间的环境变化会导致经典的监督学习，从而产生无法很好地推广到新目标分布的模型。最近，已经开发了许多发现不变预测分布的解决方案。其中，基于图的方法不需要来自目标环境的数据，并且可以捕获比找到稳定特征集的替代方法更稳定的信息。但是，这些方法假定数据生成过程以完整的因果图的形式知道，这通常不是这种情况。在本文中，我们提出了I-Spec，这是一个端到端框架，通过使用数据来学习部分祖先图（PAG）来解决此缺点。使用PAG，我们开发了一种算法，该算法确定了对声明的偏移稳定的介入分布；这包含现有方法，这些方法找到了稳定的功能集，而稳定的功能集则不太准确。我们将I-Spec应用于死亡率预测问题，以表明它可以学习一个模型，该模型在不需要预先了解全部因果DAG的情况下可以进行变化。
Shifts in environment between development and deployment cause classical supervised learning to produce models that fail to generalize well to new target distributions. Recently, many solutions which find invariant predictive distributions have been developed. Among these, graph-based approaches do not require data from the target environment and can capture more stable information than alternative methods which find stable feature sets. However, these approaches assume that the data generating process is known in the form of a full causal graph, which is generally not the case. In this paper, we propose I-SPEC, an end-to-end framework that addresses this shortcoming by using data to learn a partial ancestral graph (PAG). Using the PAG we develop an algorithm that determines an interventional distribution that is stable to the declared shifts; this subsumes existing approaches which find stable feature sets that are less accurate. We apply I-SPEC to a mortality prediction problem to show it can learn a model that is robust to shifts without needing upfront knowledge of the full causal DAG.
