JETO: Joint Estimation of Treatment Assignment and Outcomes for Heterogeneous Treatment Effect Analysis

Heterogeneous treatment effect estimation seeks to characterize how the effect of a treatment varies across individuals with different covariates, despite only one potential outcome being observed for each unit. Classical methods primarily target the conditional average treatment effect, providing a point estimate of treatment benefit. Generative modeling of \(p(Y\mid X,W)\) extends this view to treatment-specific outcome distributions, but leaves the treatment assignment mechanism outside the generative target and does not explicitly capture structure shared between propensity and outcomes. We propose JETO, which learns the joint conditional distribution \(p(Y,W\mid X)\) with conditional Flow Matching, providing a treatment effect estimate, treatment-specific means, propensity scores, and conditional outcome densities from a single model. Across six synthetic and semi-synthetic benchmarks, JETO achieves the lowest mean relative PEHE on four datasets. Controlled experiments show that joint modeling exploits covariate-dependent structure shared by treatment assignment and outcomes, while density experiments demonstrate accurate multimodal outcome recovery and propensity estimation.