SpaCE: The Spatial Confounding Environment

Mauricio Tec, Ana Trisovic, Michelle Audirac, Sophie Mirabai Woodward, Jie Kate Hu, Naeem Khoshnevis, Francesca Dominici

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Keywords: benchmarks, spatial confounding, graph neural networks, semi-synthetic data, causal inference

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TL;DR: We introduce SpaCE, the first comprehensive toolkit providing realistic benchmark datasets and tools for evaluating causal inference methods in spatial confounding scenarios.

Abstract: 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.

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Primary Area: datasets and benchmarks

Submission Number: 8502