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

arXiv: https://arxiv.org/abs/2608.08064 · HTML: https://mlsanity.com/paper/2608.08064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 19 of 19)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Causal inference using invariant prediction: identification and confidence intervals](https://mlsanity.com/paper/1501.01332/) (2015)
- [Invariant Causal Prediction for Nonlinear Models](https://mlsanity.com/paper/1706.08576/) (2017)
- [Abstracting Causal Models](https://mlsanity.com/paper/1812.03789/) (2018)
- [A Deep Journey into Super-resolution: A survey](https://mlsanity.com/paper/1904.07523/) (2019)
- [Learning from Aggregate Observations](https://mlsanity.com/paper/2004.06316/) (2020)
- [Deconditional Downscaling with Gaussian Processes](https://mlsanity.com/paper/2105.12909/) (2021)
- [Abstraction between Structural Causal Models: A Review of Definitions and Properties](https://mlsanity.com/paper/2207.08603/) (2022)
- [Variational Learning on Aggregate Outputs with Gaussian Processes](https://mlsanity.com/paper/1805.08463/) (2018)
- [Model updating after interventions paradoxically introduces bias](https://mlsanity.com/paper/2010.11530/) (2020)
- [On the modern deep learning approaches for precipitation downscaling](https://mlsanity.com/paper/2207.00808/) (2022)
- [Hierarchical Causal Models](https://mlsanity.com/paper/2401.05330/) (2024)
- [GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding](https://mlsanity.com/paper/2502.05295/) (2025)
- [Causal discovery on vector-valued variables and consistency-guided aggregation](https://mlsanity.com/paper/2505.10476/) (2025)
- [Bipartite causal inference with interference, time series data, and a random network](https://mlsanity.com/paper/2404.04775/) (2024)
- [Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference](https://mlsanity.com/paper/2412.15128/) (2024)
- [Spatio-Temporal Hierarchical Causal Models](https://mlsanity.com/paper/2511.20558/) (2025)
- [The causal effects of modified treatment policies under network interference](https://mlsanity.com/paper/2412.02105/) (2024)

## Built on by (most cited first, 1 of 1)

- [Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations](https://mlsanity.com/paper/2610.11914/) (2026)

## 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