Causal inference via neuroevolutionary selection

Abstract

The technology is directed towards receiving training data regarding a set of observations. Each observation includes a feature set, a treatment, and an outcome. A first generation of machine learning models is trained, via the training data, to predict an outcome for a feature set of a given observation. A new generation of models is generated by selecting a subset of models from the trained first generation of models based on a fitness criteria of each model to generate an intermediate layer for use in predicting a treatment. An algorithm is applied to the selected subset of models to generate the new generation of models. Transformed training data is generated using the training data and a model of the new generation of models. The transformed training data includes, for each observation, a transformed feature set comprising a representation of the feature set in a latent space of the model.