Abstract: A classifying neural network (CNN) obtains a mixed data set of a priori information and outcomes information for treated units and untreated units. Classify units as treated or untreated, by running the CNN on the a priori information. Deliver a latent representation of the classified units from an intermediate layer of the CNN to a self-organizing map (SOM) engine. Generate an SOM based on the latent representation. Train the CNN to optimize a combined total loss of the classification and of the SOM. Estimate average treatment effect on the treated units by comparing the outcome information of the treated units to outcome information for untreated units that are nearest-neighbors of the treated units on the SOM.