DriftCF: Generative Modeling of Joint Potential Outcome Distributions via One-Step Particle Drifting

Interventions often change several dependent outcomes, making the treatment-specific joint potential-outcome distribution the relevant object for uncertainty, dependence, and joint risk. Learning this law is difficult because each unit reveals only one factual outcome vector, while the missing counterfactual distribution must preserve its complete dependency structure. Existing expressive generators obtain samples through iterative denoising, numerical integration, or ordered generation. We introduce Drifting-based CounterFactual generation (DriftCF). Causal identification turns the target into conditional joint-distribution matching; DriftCF solves it by attracting complete generated vectors toward factual outcomes and repelling particles according to the model's own concentration. Detached regression amortizes this correction into a non-autoregressive generator that produces one complete vector in one forward pass. We establish the field's smoothed-score form, target fixed point, and amortized transport property. Experiments test joint-law recovery, dependence, the attraction–repulsion mechanism, and one-step efficiency on multi-outcome benchmarks and an online incentive-allocation study. Code is available in the Supplementary Material.