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.


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.

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