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:分配变化中的学习是一项具有挑战性的任务。 一种原则性做法是通过结构性因果模型来利用差异性原则。 但是, 当响应被干预时, 差异性原则被违反, 使得它成为一个困难的环境。 在最近的一项工作中, 变量性匹配财产已经开发出来, 以揭示这一情景, 并展示出有希望的表现 。 在这项工作中, 我们通过开发一个具有内在宽度的高度问题, 将差异性匹配财产推广到一个重要环境, 只有目标被干预时, 我们建议一种更稳健、 计算效率更高的算法, 利用Lasso的变种, 改进现有的算法 。</s>