CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data
Gerrit Großmann, Sumantrak Mukherjee, Sebastian J. Vollmer
cs.LG
Submitted: 2026-08-08
Updated: 2026-08-11
Code: https://github.com/gerritgr/clam
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: 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
Terminology
Abstract
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
Sources
- Abstraction between Structural Causal Models: A Review of Definitions and Properties
- Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference
- Spatiotemporal causal inference with arbitrary spillover and carryover effects: Airstrikes and insurgent violence in the Iraq War
- Bipartite causal inference with interference, time series data, and a random network
- Causal discovery on vector-valued variables and consistency-guided aggregation
- The causal effects of modified treatment policies under network interference
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