A major barrier to deploying current machine learning models lies in their non-reliability to dataset shifts. To resolve this problem, most existing studies attempted to transfer stable information to unseen environments. Particularly, independent causal mechanisms-based methods proposed to remove mutable causal mechanisms via the do-operator. Compared to previous methods, the obtained stable predictors are more effective in identifying stable information. However, a key question remains: which subset of this whole stable information should the model transfer, in order to achieve optimal generalization ability? To answer this question, we present a comprehensive minimax analysis from a causal perspective. Specifically, we first provide a graphical condition for the whole stable set to be optimal. When this condition fails, we surprisingly find with an example that this whole stable set, although can fully exploit stable information, is not the optimal one to transfer. To identify the optimal subset under this case, we propose to estimate the worst-case risk with a novel optimization scheme over the intervention functions on mutable causal mechanisms. We then propose an efficient algorithm to search for the subset with minimal worst-case risk, based on a newly defined equivalence relation between stable subsets. Compared to the exponential cost of exhaustively searching over all subsets, our searching strategy enjoys a polynomial complexity. The effectiveness and efficiency of our methods are demonstrated on synthetic data and the diagnosis of Alzheimer's disease.

翻译：部署当前机器学习模型的一个主要障碍在于它们对于数据集变化的不可靠性。为了解决这一问题,大多数现有研究试图将稳定的信息转移到看不见的环境。 特别是, 以独立因果机制为基础的方法建议通过 do- operator 清除可变因果机制。 与以往的方法相比, 获得的稳定预测器在确定稳定信息方面更为有效。 然而, 关键问题仍然是: 模型传输时, 整个稳定信息中的哪个子集, 以实现最佳的概括化能力? 为了回答这个问题, 我们从因果角度提出一个全面的微缩分析。 具体地说, 我们首先为整个稳定的数据集提供一个图形化条件, 最优化。 当这个条件失败时, 我们惊讶地发现一个这样的示例: 这个稳定的数据集, 虽然可以充分利用稳定的信息, 并不是最佳的转移机制。 为了确定本案下的最佳子集, 我们提议对最坏的风险做出一个新颖的优化计划。 我们然后提出一个高效的算法, 以最小的最坏的风险来搜索这个子集, 以新定义的稳定子集之间的等对应关系为基础。 将我们所展示的模型的复杂度与最接近性分析方法。