Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
This addresses the challenge of causal inference in spatial settings for researchers and practitioners dealing with observational data, representing an incremental improvement by focusing on a specific methodological bottleneck.
The paper tackles the problem of inferring spatially varying causal effects from observational data with unmeasured confounding, by developing a method that eliminates the nuisance function and mitigates errors-in-variables, resulting in robust and accurate inference as demonstrated on synthetic and real data from Germany and the US.
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.