Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Abstract
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.
Cite
Text
Osama et al. "Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding." International Conference on Machine Learning, 2019.Markdown
[Osama et al. "Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/osama2019icml-inferring/)BibTeX
@inproceedings{osama2019icml-inferring,
title = {{Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding}},
author = {Osama, Muhammad and Zachariah, Dave and Schön, Thomas B.},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {4942-4950},
volume = {97},
url = {https://mlanthology.org/icml/2019/osama2019icml-inferring/}
}