I-SPEC：一个学习可移植、平移稳定模型的端到端框架
I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models
摘要：
开发和部署之间的环境变化导致经典的监督学习产生的模型不能很好地推广到新的目标分布。最近，已经开发了许多找到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。然而，这些方法假设数据生成过程以完整因果图的形式已知，这通常不是情况。在本文中，我们提出了I-SPEC，一个端到端的框架，通过使用数据来学习部分祖先图（PAG）来解决这个缺点。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;这包含了找到不太准确的稳定特征集的现有方法。我们将I-SPEC应用于死亡率预测问题，以证明它可以学习一个对变化具有鲁棒性的模型，而无需预先了解完整的因果DAG。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
