# SpaCE: The Spatial Confounding Environment

arXiv: https://arxiv.org/abs/2312.00710 · HTML: https://mlsanity.com/paper/2312.00710/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 66 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 27 of 27)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition](https://mlsanity.com/paper/1707.02641/) (2017)
- [Learning Counterfactual Representations for Estimating Individual Dose-Response Curves](https://mlsanity.com/paper/1902.00981/) (2019)
- [VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments](https://mlsanity.com/paper/2103.07861/) (2021)
- [Using simulation studies to evaluate statistical methods](https://mlsanity.com/paper/1712.03198/) (2017)
- [RealCause: Realistic Causal Inference Benchmarking](https://mlsanity.com/paper/2011.15007/) (2020)
- [Identification and estimation of treatment and interference effects in observational studies on networks](https://mlsanity.com/paper/1609.06245/) (2016)
- [Causal Inference under Networked Interference and Intervention Policy Enhancement](https://mlsanity.com/paper/2002.08506/) (2020)
- [Using Embeddings to Correct for Unobserved Confounding in Networks](https://mlsanity.com/paper/1902.04114/) (2019)
- [Causal Inference Under Interference And Network Uncertainty](https://mlsanity.com/paper/1907.00221/) (2019)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Causal Diagrams for Interference](https://mlsanity.com/paper/1403.1239/) (2014)
- [Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq](https://mlsanity.com/paper/2003.13555/) (2020)
- [How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference](https://mlsanity.com/paper/2010.03051/) (2020)
- [Spatial causal inference in the presence of unmeasured confounding and interference](https://mlsanity.com/paper/2303.08218/) (2023)
- [Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching](https://mlsanity.com/paper/1610.07583/) (2016)
- [U.S. Broadband Coverage Data Set: A Differentially Private Data Release](https://mlsanity.com/paper/2103.14035/) (2021)
- [Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies](https://mlsanity.com/paper/2209.12316/) (2022)
- [Spatial+: a novel approach to spatial confounding](https://mlsanity.com/paper/2009.09420/) (2020)
- [A causal inference framework for spatial confounding](https://mlsanity.com/paper/2112.14946/) (2021)
- [Consistency of common spatial estimators under spatial confounding](https://mlsanity.com/paper/2308.12181/) (2023)
- [Restricted Spatial Regression Methods: Implications for Inference](https://mlsanity.com/paper/1905.09371/) (2019)
- [Evaluating recent methods to overcome spatial confounding](https://mlsanity.com/paper/2210.07046/) (2022)
- [Re-thinking Spatial Confounding in Spatial Linear Mixed Models](https://mlsanity.com/paper/2301.05743/) (2023)

## Built on by (most cited first, 3 of 3)

- [Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference](https://mlsanity.com/paper/2510.08762/) (2025)
- [Spatio-temporal Causal Learning for Streamflow Forecasting](https://mlsanity.com/paper/2411.17937/) (2024)
- [Optimizing Heat Alert Issuance with Reinforcement Learning](https://mlsanity.com/paper/2312.14196/) (2023)

## Abstract (arXiv)

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.