Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance principle is violated when the response is intervened, making it a difficult setting. In a recent work, the invariant matching property has been developed to shed light on this scenario and shows promising performance. In this work, by formulating a high-dimensional problem with intrinsic sparsity, we generalize the invariant matching property for an important setting when only the target is intervened. We propose a more robust and computation-efficient algorithm by leveraging a variant of Lasso, improving upon the existing algorithms.

翻译：在分布偏移下进行学习是一项具有挑战性的任务。利用结构因果模型中的不变性原则是一种有原则性的方法。然而，当响应变量受到干预时，不变性原则被违反，这使得该场景变得困难。在最近的一项工作中，开发了不变匹配性质以阐明这一场景，并展现出有前景的性能。在本工作中，通过构建一个具有内在稀疏性的高维问题，我们推广了仅在目标变量受干预这一重要场景下的不变匹配性质。我们提出了一种利用Lasso变体的更稳健且计算高效的算法，改进了现有算法。