Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding

01/28/2019
by   Muhammad Osama, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset