Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector autoregressive models. We present a unifying framework for parameter identifiability and estimation under subsampling and mixed frequencies when the noise, or shocks, is non-Gaussian. By studying the structural case, we develop identifiability and estimation methods for the causal structure of lagged and instantaneous effects at the desired time scale. We further derive an exact expectation-maximization algorithm for inference in both subsampled and mixed-frequency settings. We validate our approach in simulated scenarios and on a climate and an econometric dataset.

中文翻译：

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子采样和混频时间序列结构向量自回归模型的可识别性和估计

多元时间序列中的因果推断具有挑战性，因为采样率可能不如因果相互作用的时间尺度那么快，因此观察到的序列是所需序列的子采样版本。此外，可以以不同的采样率观察序列，从而产生混合频率序列。为了确定因果尺度上序列之间的瞬时和滞后效应，我们采用基于模型的方法，该方法依赖于结构向量自回归模型。当噪声或冲击为非高斯时，我们提出了一个统一的框架，用于在子采样和混合频率下进行参数识别和估计。通过研究结构案例，我们开发了在所需时间尺度上滞后和瞬时效应的因果结构的可识别性和估计方法。我们进一步推导出精确的期望最大化算法，用于子采样和混合频率设置中的推理。我们在模拟场景以及气候和计量经济数据集上验证了我们的方法。