Robust Optimization in Causal Models and G-Causal Normalizing Flows
Gabriele Visentin and Patrick Cheridito
在本文中,我们表明因果模型中的介入稳健优化问题在G-因果Wasserstein距离下是连续的,但在标准Wasserstein距离下可能是不连续的。 这强调了在为此类任务增加数据时使用尊重因果结构的生成模型的重要性。 为此,我们提出了一种新的归一化流架构,满足因果结构模型的通用近似属性,并可以有效地训练,以最小化G因果关系Wasserstein距离。 经验上,我们证明我们的模型在因果因子模型中的因果回归和均值-变量组合优化中优于标准(非因果关系)生成模型。
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 efficien...
