论文标题
带有潜在共同原因的时间序列的因果特征选择的必要条件
Necessary and sufficient conditions for causal feature selection in time series with latent common causes
论文作者
论文摘要
我们研究了时间序列上直接和间接原因的识别，并在存在潜在变量的情况下提供条件，在某些图形约束下，我们被证明是必要和足够的。我们的理论结果和估计算法需要为每个观察到的候选时间序列进行两个条件独立性测试，以确定是否是观察到的目标时间序列的原因。我们在模拟以及实际数据中提供实验结果。我们的结果表明，我们的方法会导致非常低的假阳性和相对较低的假负率，表现优于广泛使用的Granger因果关系。
We study the identification of direct and indirect causes on time series and provide conditions in the presence of latent variables, which we prove to be necessary and sufficient under some graph constraints. Our theoretical results and estimation algorithms require two conditional independence tests for each observed candidate time series to determine whether or not it is a cause of an observed target time series. We provide experimental results in simulations, as well as real data. Our results show that our method leads to very low false positives and relatively low false negative rates, outperforming the widely used Granger causality.
