Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding

Muhammad Osama, Dave Zachariah, Thomas B. Schön
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4942-4950, 2019.

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 this Paper

BibTeX
@InProceedings{pmlr-v97-osama19a, title = {Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding}, author = {Osama, Muhammad and Zachariah, Dave and Sch{\"o}n, Thomas B.}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {4942--4950}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/osama19a/osama19a.pdf}, url = {https://proceedings.mlr.press/v97/osama19a.html}, 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.} }
Endnote
%0 Conference Paper %T Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding %A Muhammad Osama %A Dave Zachariah %A Thomas B. Schön %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-osama19a %I PMLR %P 4942--4950 %U https://proceedings.mlr.press/v97/osama19a.html %V 97 %X 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.
APA
Osama, M., Zachariah, D. & Schön, T.B.. (2019). Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:4942-4950 Available from https://proceedings.mlr.press/v97/osama19a.html.

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