提出新型因果生成流，提升因果优化任务的数据增强效果
Robust Optimization in Causal Models and G-Causal Normalizing Flows
- 基于因果结构设计可逼近任意因果模型的生成流
- 在因果回归与投资组合优化中优于传统生成模型
- 使用G-因果Wasserstein距离确保优化过程稳定
本文证明，在因果模型中，基于干预的鲁棒优化问题在G-因果Wasserstein距离下是连续的，但在标准Wasserstein距离下可能不连续。这表明数据增强时需使用尊重因果结构的生成模型。为此，我们提出一种新的归一化流架构，具备对因果结构模型的通用逼近能力，并能高效最小化G-因果Wasserstein距离。实验表明，该模型在因果回归和因果因子模型中的均值-方差投资组合优化任务中，显著优于标准（非因果）生成模型。
原文摘要 · Abstract (English)
In this paper, we show that interventionally robust optimization problems in causal models are continuous under the $G$-causal Wasserstein distance, but may be discontinuous under the standard Wasserstein distance. This highlights the importance of using generative models that respect the causal structure when augmenting data for such tasks. To this end, we propose a new normalizing flow architecture that satisfies a universal approximation property for causal structural models and can be efficiently trained to minimize the $G$-causal Wasserstein distance. Empirically, we demonstrate that our model outperforms standard (non-causal) generative models in data augmentation for causal regression and mean-variance portfolio optimization in causal factor models.
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