Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
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Abstract (arXiv)
Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outcome, and (2) interference from nearby treatments that violate standard no-interference assumptions. While existing methods typically address one by assuming away the other, we show they are deeply connected: interference reveals structure in the latent confounder. Leveraging this insight, we propose the Spatial Deconfounder, a two-stage method that reconstructs a substitute confounder from local treatment vectors using a conditional variational autoencoder (C-VAE) with a spatial prior, then estimates causal effects with a flexible outcome model. We show that this enables nonparametric identification of direct and spillover effects under weak assumptions--without multiple treatment types or a known latent-field model. Empirically, we extend SpaCE, a benchmark suite for spatial confounding, to include treatment interference, and show that the Spatial Deconfounder consistently improves effect estimation across real-world environmental health and social science datasets. By turning local interference into a multi-cause proxy for latent spatial confounding, our framework advances robust causal inference for spatial data.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 48 of its 77 references that are in the map. First posted on arXiv 9 Oct 2025.
Builds on
48 of 77 references.
References · 48
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2015Variational Inference with Normalizing Flowscited by 2,161
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
- 2021Variational Diffusion Modelscited by 1,145
36 more
- 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
- 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 368
- 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
- 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
- 2018The Blessings of Multiple Causescited by 103
- 2020Causal Autoregressive Flowscited by 97
- 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 97
- 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
- 2019Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounderscited by 74
- 2016Auxiliary Deep Generative Modelscited by 72
- 2021Identifiable Deep Generative Models via Sparse Decodingcited by 54
- 2023Causal normalizing flows: from theory to practicecited by 49
- 2016Identification and estimation of treatment and interference effects in observational studies on networkscited by 43
- 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
- 2022Embrace the Gap: VAEs Perform Independent Mechanism Analysiscited by 28
- 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
- 2021Sequential Deconfounding for Causal Inference with Unobserved Confounderscited by 24
- 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
- 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
- 2024Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learningcited by 19
- 2017Auto-G-Computation of Causal Effects on a Networkcited by 16
- 2023Spatial causal inference in the presence of unmeasured confounding and interferencecited by 9
- 2025DeCaFlow: A deconfounding causal generative modelcited by 8
- 2016Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matchingcited by 7
- 2019Comment: Reflections on the Deconfoundercited by 6
- 2023Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Datacited by 5
- 2025GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confoundingcited by 5
- 2020Spatial+: a novel approach to spatial confoundingcited by 4
- 2021Towards Principled Causal Effect Estimation by Deep Identifiable Modelscited by 4
- 2024Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interferencecited by 4
- 2015Interference and Sensitivity Analysiscited by 3
- 2023SpaCE: The Spatial Confounding Environmentcited by 3
- 2020A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model outputcited by 2
- 2020Bipartite Interference and Air Pollution Transport: Estimating Health Effects of Power Plant Interventionscited by 2
- 2024An Instrumental Variables Framework to Unite Spatial Confounding Methodscited by 2
- 2023Demystifying Spatial Confoundingcited by 1
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