  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.
