Deep Counterfactual Networks with Propensity-Dropout

1 code implementation • 19 Jun 2017 • Ahmed M. Alaa, Michael Weisz, Mihaela van der Schaar

The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers.

Causal Inference counterfactual +1