论文标题
学习反面不变的预测指标
Learning Counterfactually Invariant Predictors
论文作者
论文摘要
事实证明，反事实不变性（CI）的概念对于在现实世界中公平，健壮且可推广的预测指标被证明至关重要。我们提出了图形标准，该标准在观察分布的条件独立性方面产生了足够的条件，即预测变量反而不变。为了学习此类预测指标，我们提出了一个模型不合时宜的框架，称为反事实不变预测（CIP），建立在希尔伯特·史密特（Hilbert-Schmidt）条件独立标准（HSCIC）的基础上，这是一种基于内核的条件依赖度量。我们的实验结果表明，CIP在在包括标量和多变量设置在内的各种模拟和现实数据集中实现反事实不变性的有效性。
Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world. We propose graphical criteria that yield a sufficient condition for a predictor to be counterfactually invariant in terms of a conditional independence in the observational distribution. In order to learn such predictors, we propose a model-agnostic framework, called Counterfactually Invariant Prediction (CIP), building on the Hilbert-Schmidt Conditional Independence Criterion (HSCIC), a kernel-based conditional dependence measure. Our experimental results demonstrate the effectiveness of CIP in enforcing counterfactual invariance across various simulated and real-world datasets including scalar and multi-variate settings.
