mlsanityData to 7 Oct 2026

CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data

arXiv 2608.08064 ↗First posted 8 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated interventions and outcomes. We introduce CLAM, a method for estimating localized causal effects from coarse observations by exploiting high-resolution contextual covariates that modulate these effects. By jointly learning the causal mechanism and a disaggregation mapping, CLAM captures interactions that are missed when addressing these problems independently. The method supports localized effect estimation, counterfactual reasoning, and principled outcome disaggregation, and reliably captures spatially varying causal effects across diverse settings. This is particularly relevant for applications such as public health and environmental policy, where decisions are made at broad scales despite substantial local heterogeneity. Code is available at https://github.com/gerritgr/clam

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References · 19

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7 more
  1. 2024Hierarchical Causal Modelscited by 7
  2. 2025GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confoundingcited by 5
  3. 2024Bipartite causal inference with interference, time series data, and a random networkcited by 3
  4. 2025Causal discovery on vector-valued variables and consistency-guided aggregationcited by 3
  5. 2024Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inferencecited by 2
  6. 2025Spatio-Temporal Hierarchical Causal Modelscited by 2
  7. 2024The causal effects of modified treatment policies under network interferencecited by 1

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