Generative Learner for Distributional Causal Effects

We propose a generative learner for estimating conditional average treatment effects and characterizing the full distribution of these effects. The learner takes the form of a multi-head feed-forward neural network with three jointly estimated subnetworks: propensity score, baseline outcome, and the