关于从时间聚合的独立同分布数据中恢复因果关系的研究
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
AI总结：
本文研究时间聚合对瞬时因果发现的影响，形式化提出函数一致性与条件独立一致性，证明聚合尤其在完全非线性情形下会严重扭曲结果，但部分线性或适当先验可使因果关系仍可恢复。
摘要：
我们在一般设定下考察时间聚合对瞬时（非时间）因果发现的影响。其动机在于观察到真实因果时间滞后通常显著短于观测间隔。这种差异导致高度聚合，使时延因果性消失，而瞬时依赖性显现。尽管我们期望这种瞬时依赖在某种意义上与真实因果关系保持一致，以使发现结果有意义，但尚不清楚需要何种类型的一致性，以及何时满足这种一致性。我们以形式化方式提出了函数一致性和条件独立一致性，分别对应基于函数因果模型的方法和基于条件独立的方法，并给出这些一致性成立的条件。我们在理论和实验上表明，因果发现结果可能因聚合而严重失真，尤其是在完全非线性情形下；我们还发现，如果存在部分线性或适当先验，因果关系仍可从聚合数据中恢复。我们的发现表明，学界在解释来自此类数据的因果发现结果时应采取谨慎细致的态度，并说明了聚合为何以及何时会扭曲因果发现方法的性能。
英文摘要：
We consider the effect of temporal aggregation on instantaneous (non-temporal) causal discovery in general setting. This is motivated by the observation that the true causal time lag is often considerably shorter than the observational interval. This discrepancy leads to high aggregation, causing time-delay causality to vanish and instantaneous dependence to manifest. Although we expect such instantaneous dependence has consistency with the true causal relation in certain sense to make the discovery results meaningful, it remains unclear what type of consistency we need and when will such consistency be satisfied. We proposed functional consistency and conditional independence consistency in formal way correspond functional causal model-based methods and conditional independence-based methods respectively and provide the conditions under which these consistencies will hold. We show theoretically and experimentally that causal discovery results may be seriously distorted by aggregation especially in complete nonlinear case and we also find causal relationship still recoverable from aggregated data if we have partial linearity or appropriate prior. Our findings suggest community should take a cautious and meticulous approach when interpreting causal discovery results from such data and show why and when aggregation will distort the performance of causal discovery methods.
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。
