DecoR：使用鲁棒回归进行去混杂的时间序列
DecoR: Deconfounding Time Series with Robust Regression
AI总结：
针对时间序列因果推断中未观测混杂因素问题，提出基于频域鲁棒线性回归的DecoR方法，改进误差上界并证明一致性，实验验证其在合成与真实数据上的有效性及对模型误设的鲁棒性。
摘要：
时间序列数据上的因果推断是一个具有挑战性的问题，尤其是在存在未观测混杂因素的情况下。本工作聚焦于估计被第三个未观测时间序列混杂的两个时间序列之间的因果效应。假设混杂因素具有频谱稀疏性，我们展示了在频域中如何将该问题构建为对抗性异常值问题。我们提出了鲁棒回归去混杂（DecoR），一种在频域中使用鲁棒线性回归来估计因果效应的新颖方法。考虑两种不同的鲁棒回归技术，我们首先改进了此类技术的估计误差现有界限。至关重要的是，我们的结果不需要对协变量进行分布假设。因此，我们可以在时间序列环境中使用它们。将这些结果应用于DecoR，我们在适当的假设下证明了DecoR估计误差的上界，这意味着一致性。我们通过对来自地球系统科学的合成数据和真实世界数据进行实验，展示了DecoR的有效性。模拟实验进一步表明，DecoR对模型误设具有鲁棒性。
英文摘要：
Causal inference on time series data is a challenging problem, especially in the presence of unobserved confounders. This work focuses on estimating the causal effect between two time series that are confounded by a third, unobserved time series. Assuming spectral sparsity of the confounder, we show how in the frequency domain this problem can be framed as an adversarial outlier problem. We introduce Deconfounding by Robust regression (DecoR), a novel approach that estimates the causal effect using robust linear regression in the frequency domain. Considering two different robust regression techniques, we first improve existing bounds on the estimation error for such techniques. Crucially, our results do not require distributional assumptions on the covariates. We can therefore use them in time series settings. Applying these results to DecoR, we prove, under suitable assumptions, upper bounds for the estimation error of DecoR that imply consistency. We demonstrate DecoR's effectiveness through experiments on both synthetic and real-world data from Earth system science. The simulation experiments furthermore suggest that DecoR is robust with respect to model misspecification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
