We explore the usage of meta-learning to derive the causal direction between variables by optimizing over a measure of distribution simplicity. We incorporate a stochastic graph representation which includes latent variables and allows for more generalizability and graph structure expression. Our model is able to learn causal direction indicators for complex graph structures despite effects of latent confounders. Further, we explore robustness of our method with respect to violations of our distributional assumptions and data scarcity. Our model is particularly robust to modest data scarcity, but is less robust to distributional changes. By interpreting the model predictions as stochastic events, we propose a simple ensemble method classifier to reduce the outcome variability as an average of biased events. This methodology demonstrates ability to infer the existence as well as the direction of a causal relationship between data distributions.

中文翻译：

---

一种识别因果图结构的元学习方法

我们探索使用元学习通过优化分布简单性的度量来推导变量之间的因果方向。我们合并了一个包含潜在变量的随机图表示，并允许更多的泛化性和图结构表达。尽管存在潜在混杂因素的影响，我们的模型仍能够学习复杂图结构的因果方向指标。此外，我们探索了我们的方法在违反我们的分布假设和数据稀缺性方面的稳健性。我们的模型对适度的数据稀缺性特别稳健，但对分布变化的稳健性较差。通过将模型预测解释为随机事件，我们提出了一种简单的集成方法分类器，以将结果变异性降低为偏差事件的平均值。