Causal discovery is the subfield of causal inference concerned with estimating the structure of cause-and-effect relationships in a system of interrelated variables, as opposed to quantifying the strength or describing the form of causal effects. As interest in causal discovery builds in fields such as ecology, public health, and environmental sciences where data are regularly collected with spatial and temporal structures, approaches must evolve to manage autocorrelation and complex confounding. As it stands, the few proposed causal discovery algorithms for spatiotemporal data require summarizing across locations, ignore spatial autocorrelation, and/or scale poorly to high dimensions. Here, we introduce our developing framework that extends time-series causal discovery to systems with spatial structure, building upon work on causal discovery across contexts and methods for handling spatial confounding in causal effect estimation. We close by outlining remaining gaps in the literature and directions for future research.

翻译：因果关系发现是因果推断的一个子领域，旨在估计相互关联变量系统中因果关系的结构，而非量化因果效应的强度或描述其形式。随着生态学、公共卫生、环境科学等经常采集具有空间和时间结构数据的领域对因果关系发现的兴趣日益增长，相关方法必须发展以应对自相关和复杂混杂问题。目前，针对时空数据提出的少数因果关系发现算法需要对不同地点进行汇总，忽略了空间自相关，并且/或者难以扩展到高维场景。本文介绍了我们正在开发的框架，该框架将时间序列因果关系发现扩展到具有空间结构的系统，并基于跨情境因果关系发现以及因果效应估计中处理空间混杂的方法展开研究。最后，我们指出了文献中尚未解决的问题及未来研究方向。