Outcome-Guided Counterfactuals from a Jointly Trained Generative Latent Space

In general, techniques are described for generating counterfactuals using a machine learning system that implements a generative model. In an example, a method includes receiving, by a trained generative machine learning model, an input query, wherein the generative machine learning model is trained by jointly encoding a plurality of input observations and a plurality of outcome variables based on the plurality of input observations; generating, by the trained generative machine learning model, latent representation of the input query; and transforming, by the trained generative machine learning system, the latent representation of the input query to generate a counterfactual related to the received input query, wherein the generated counterfactual meets a predefined outcome criteria.