# Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding

arXiv: https://arxiv.org/abs/1901.09919 · HTML: https://mlsanity.com/paper/1901.09919/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Adaptive Multi-Source Causal Inference](https://mlsanity.com/paper/2105.14877/) (2021)
- [Graph-Split Bayesian Causal Forest for Spatial Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2609.36046/) (2026)

## Abstract (arXiv)

We address the problem of inferring the causal effect of an exposure on an outcome across space, using observational data. The data is possibly subject to unmeasured confounding variables which, in a standard approach, must be adjusted for by estimating a nuisance function. Here we develop a method that eliminates the nuisance function, while mitigating the resulting errors-in-variables. The result is a robust and accurate inference method for spatially varying heterogeneous causal effects. The properties of the method are demonstrated on synthetic as well as real data from Germany and the US.