论文标题
通过仿制干预措施使用模型不变性的因果发现
Causal Discovery using Model Invariance through Knockoff Interventions
论文作者
论文摘要
原因效应分析对于了解系统的潜在机制至关重要。我们建议通过对预测因子的干预措施来利用模型不变性，以推断非线性多元时间序列中的因果关系。我们使用DeePar对时间序列进行非线性相互作用进行建模，然后使用基于仿基的干预措施将模型暴露于不同的环境中，以测试模型不变性。仿冒样品是成对交换的，分配的，并且在不知道响应的情况下生成的统计上的无效变量。我们测试模型不变性，我们表明在非毒物预测因子的干预措施后，响应残差的分布不会显着变化。我们评估了我们的实际和合成生成时间序列的方法。总体而言，我们的方法的表现优于其他广泛使用的因果关系方法，即Var Granger因果关系，Varlingam和PCMCI+。
Cause-effect analysis is crucial to understand the underlying mechanism of a system. We propose to exploit model invariance through interventions on the predictors to infer causality in nonlinear multivariate systems of time series. We model nonlinear interactions in time series using DeepAR and then expose the model to different environments using Knockoffs-based interventions to test model invariance. Knockoff samples are pairwise exchangeable, in-distribution and statistically null variables generated without knowing the response. We test model invariance where we show that the distribution of the response residual does not change significantly upon interventions on non-causal predictors. We evaluate our method on real and synthetically generated time series. Overall our method outperforms other widely used causality methods, i.e, VAR Granger causality, VARLiNGAM and PCMCI+.
