למדן יוצרי: חידוש להערכת תוצאות טיפול

תקציר מקורי באנגליתarXiv:2503.17894v4 Announce Type: replace-cross Abstract: 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 conditional average treatment effect. Here, the treatment effect subnetwork parameterizes the conditional quantile function via a compositional architecture in which covariate representation and cosine quantile embeddings are combined through element-wise multiplication. We then recover the conditional average treatment effect as an integral over conditional quantile treatment effects. Under the classical causal assumptions within the Ne