Discovering Causal Relationships Between Time Series With Spatial Structure

TL;DR

This paper introduces a new framework for discovering causal relationships in spatiotemporal data, addressing challenges like spatial autocorrelation and high-dimensionality, with potential applications in ecology, public health, and environmental sciences.

Contribution

It extends existing causal discovery methods to incorporate spatial structure, improving accuracy and scalability for analyzing complex spatiotemporal systems.

Findings

Framework effectively captures causal relationships in spatial data

Addresses spatial autocorrelation and confounding issues

Scalable to high-dimensional spatiotemporal datasets

Abstract

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.