论文标题
摊销的因果发现：学会从时间序列数据推断因果图
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
论文作者
论文摘要
在时间序列数据上，大多数因果发现方法每当遇到新的基本因果图中的样本时，它们都适合新模型。但是，这些样本通常共享相关信息，这些信息在遵循这种方法时会丢失。具体而言，不同的样本可以共享描述其因果关系影响的动力学。我们提出了摊销的因果发现，这是一个新颖的框架，该框架利用了这种共同的动态来学会从时间序列数据中推断因果关系。这使我们能够训练一个单一的，摊销的模型，该模型渗透到具有不同基本因果图的样本中的因果关系，从而利用共享的动态信息。我们在实验上证明，这种方法是作为变异模型实现的，可以显着改善因果发现绩效，并显示如何在增加的噪声和隐藏的混淆下扩展其表现良好。
On time-series data, most causal discovery methods fit a new model whenever they encounter samples from a new underlying causal graph. However, these samples often share relevant information which is lost when following this approach. Specifically, different samples may share the dynamics which describe the effects of their causal relations. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus leverages the shared dynamics information. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under added noise and hidden confounding.
