A fundamental task in AI is providing performance guarantees for predictions made in unseen domains. In practice, there can be substantial uncertainty about the distribution of new data, and corresponding variability in the performance of existing predictors. Building on the theory of partial identification and transportability, this paper introduces new results for bounding the value of a functional of the target distribution, such as the generalization error of a classifier, given data from source domains and assumptions about the data generating mechanisms, encoded in causal diagrams. Our contribution is to provide the first general estimation technique for transportability problems, adapting existing parameterization schemes such Neural Causal Models to encode the structural constraints necessary for cross-population inference. We demonstrate the expressiveness and consistency of this procedure and further propose a gradient-based optimization scheme for making scalable inferences in practice. Our results are corroborated with experiments.

翻译：人工智能中的一个基本任务是为在未见领域中所做预测提供性能保证。在实践中，新数据的分布可能存在显著不确定性，现有预测器的性能也相应存在变异性。基于部分可识别性与可迁移性理论，本文提出了新的边界结果：在给定源领域数据及关于数据生成机制的假设（以因果图形式编码）的条件下，对目标分布泛函（如分类器的泛化误差）的值进行界定。我们的贡献在于首次为可迁移性问题提供了通用估计技术，通过适配神经因果模型等现有参数化方案来编码跨群体推断所需的结构约束。我们证明了该方法的表达一致性与一致性，并进一步提出基于梯度的优化方案以实现实际可扩展推断。实验结果验证了我们的理论发现。