Inferring causal relationships in observational time series data is an important task when interventions cannot be performed. Granger causality is a popular framework to infer potential causal mechanisms between different time series. The original definition of Granger causality is restricted to linear processes and leads to spurious conclusions in the presence of a latent confounder. In this work, we harness the expressive power of recurrent neural networks and propose a deep learning-based approach to model non-linear Granger causality by directly accounting for latent confounders. Our approach leverages multiple recurrent neural networks to parameterise predictive distributions and we propose the novel use of a dual-decoder setup to conduct the Granger tests. We demonstrate the model performance on non-linear stochastic time series for which the latent confounder influences the cause and effect with different time lags; results show the effectiveness of our model compared to existing benchmarks.
翻译:在无法实施干预时,在观察时间序列数据中推断因果关系是一项重要任务。当无法实施干预时,引因性是一个在不同的时间序列中推导潜在因果关系机制的流行框架。Granger因果关系的最初定义仅限于线性过程,在潜在困惑者在场的情况下导致虚假的结论。在这项工作中,我们利用经常性神经网络的表达力,提出一种深层次的基于学习的方法,通过直接计算潜在混淆者来模拟非线性引因性。我们的方法利用多个经常性神经网络来参数化预测分布,我们提议以新颖方式使用双分解器来进行Granger测试。我们展示了非线性随机时间序列的模型性能,而潜在聚合者在其中以不同的时间滞后影响因果关系;结果显示了我们模型与现有基准相比的有效性。