Given a causal graph representing the data-generating process shared across different domains/distributions, enforcing sufficient graph-implied conditional independencies can identify domain-general (non-spurious) feature representations. For the standard input-output predictive setting, we categorize the set of graphs considered in the literature into two distinct groups: (i) those in which the empirical risk minimizer across training domains gives domain-general representations and (ii) those where it does not. For the latter case (ii), we propose a novel framework with regularizations, which we demonstrate are sufficient for identifying domain-general feature representations without a priori knowledge (or proxies) of the spurious features. Empirically, our proposed method is effective for both (semi) synthetic and real-world data, outperforming other state-of-the-art methods in average and worst-domain transfer accuracy.

在给定描述不同领域/分布共享的数据生成过程的因果图的基础上，通过强制执行足够的图推断条件独立性可以确定领域通用（非虚假的）特征表示。针对标准的输入-输出预测设置，我们将文献中考虑的图集合分类为两个不同的组：（i）那些在训练领域中的经验风险最小化器给出领域通用表示和（ii）那些不给出的情况。对于后一种情况（ii），我们提出了一个新的具有正则化的框架，该框架在没有关于虚假特征的先验知识（或代理）的情况下证明了足够用于确定领域通用特征表示。在实证方面，我们的提出的方法在（半）合成和真实数据方面表现出色，优于其他最先进的方法的平均和最差领域转移准确性。

因果启发式正则化实现域通用表示