Controlling for spatial confounding and spatial interference in causal inference: Model selection advice from a computational experiment

Keywords: causal inference, spatial analysis, spatial dependence
Abstract Type: Paper Abstract

Abstract

Causal inference is a rapidly growing field of statistics that applies logical reasoning to statistical inference to estimate causal relationships. Spatial data poses several problems in causal inference—namely, spatial confounding (spatial heterogeneity) and interference (spatial dependence)—that require different strategies when designing causal models. Given the blossoming literature on spatial causal inference, this research analyzes the usage of spatial causal models under a priori knowledge and a priori ignorance of the spatial structure of data. In the first case, we validate that spatial causal models accurately capture treatment effects in the presence of spatial confounding and interference. In the second case, we develop practical workflow guidelines based on the relative performance of spatial and nonspatial causal models across data scenarios. In parallel, we build a Python software package of spatial causal models and data simulators to facilitate the widespread use of these models and to enable reproduction of this work.