1 code implementation • 19 May 2022 • Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew L. Beam
In this work, we introduce a flexible and scalable method based on a deep neural network to estimate causal effects in the presence of unmeasured confounding using proximal inference.
