Spatial confounding poses a significant challenge in scientific studies
involving spatial data, where unobserved spatial variables can influence both
treatment and outcome, possibly leading to spurious associations. To address
this problem, we introduce SpaCE: The Spatial Confounding Environment, the
first toolkit to provide realistic benchmark datasets and tools for
systematically evaluating causal inference methods designed to alleviate
spatial confounding. Each dataset includes training data, true counterfactuals,
a spatial graph with coordinates, and smoothness and confounding scores
characterizing the effect of a missing spatial confounder. It also includes
realistic semi-synthetic outcomes and counterfactuals, generated using
state-of-the-art machine learning ensembles, following best practices for
causal inference benchmarks. The datasets cover real treatment and covariates
from diverse domains, including climate, health and social sciences. SpaCE
facilitates an automated end-to-end pipeline, simplifying data loading,
experimental setup, and evaluating machine learning and causal inference
models. The SpaCE project provides several dozens of datasets of diverse sizes
and spatial complexity. It is publicly available as a Python package,
encouraging community feedback and contributions.

通过 SpaCE 项目，我们提供了一种解决空间混淆问题的工具包，其中包括具有实际性基准数据集和工具的真实合成结果，用于评估旨在减轻空间混淆的因果推断方法，旨在促进机器学习和因果推断模型的实验和评估。