Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding

Understand

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.