不变量搜索
Invariant Ancestry Search
摘要：
最近，已经提出了利用预测模型相对于变化的环境的不变性来推断响应变量的因果亲本的子集的方法。如果环境只影响很少的潜在机制，例如，由不变因果预测（ICP）识别的子集可能很小，甚至是空的。我们引入了最小不变性的概念，提出了不变祖先搜索（IAS）。在其总体版本中，IAS输出一个集，该集仅包含响应的祖先，并且是ICP输出的超集。当应用于数据时，如果不变性的基本检验具有渐近水平和功效，则相应的保证渐近成立。我们开发了可扩展的算法，并在模拟和真实数据上进行实验。
英文摘要：
Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response variable. If the environments influence only few of the underlying mechanisms, the subset identified by invariant causal prediction (ICP), for example, may be small, or even empty. We introduce the concept of minimal invariance and propose invariant ancestry search (IAS). In its population version, IAS outputs a set which contains only ancestors of the response and is a superset of the output of ICP. When applied to data, corresponding guarantees hold asymptotically if the underlying test for invariance has asymptotic level and power. We develop scalable algorithms and perform experiments on simulated and real data.
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。
