# SCOUT

SCOUT compresses high-dimensional, noisy baseline variables into at most two adjustment scores and learns a treatment-responsive composite outcome inside that low-dimensional space, yielding average and heterogeneous treatment-effect estimates from observational data. Among 481 STEP-BD participants with bipolar disorder, the learned composite put its largest weights on nocturia, tremor, polyuria and nausea/vomiting, with a mean effect of 0.250 and Holm-adjusted values of 0.0026 for the average effect and 0.033 for heterogeneity. On synthetic and semi-synthetic data its average-effect error was close to CV-TMLE while CATE error for its score-indexed target was substantially lower.

SCOUT compresses high-dimensional, noisy baseline variables into at most two adjustment scores and learns a treatment-responsive composite outcome inside that low-dimensional space, yielding average and heterogeneous treatment-effect estimates from observational data. Among 481 STEP-BD participants with bipolar disorder, the learned composite put its largest weights on nocturia, tremor, polyuria and nausea/vomiting, with a mean effect of 0.250 and Holm-adjusted values of 0.0026 for the average effect and 0.033 for heterogeneity. On synthetic and semi-synthetic data its average-effect error was close to CV-TMLE while CATE error for its score-indexed target was substantially lower.

The work proposes SCOUT, an offline multi-agent reinforcement learning framework that separately trains a flow-matching behavioral prior and a decomposed value function, then at test time transports behavioral samples toward high-value regions via Stein variational gradient descent, using the number of transport steps in place of a fixed regularization coefficient; under the individual-global-max (IGM) principle it proves a single-term KL bound on the joint soft-value gap that vanishes as transport converges, with an irreducible additive residual proportional to the IGM violation; empirically it achieves the best average performance across discrete and continuous offline MARL benchmarks and yields improvements in all offline-to-online configurations.

The work proposes SCOUT, an offline multi-agent reinforcement learning framework that separately trains a flow-matching behavioral prior and a decomposed value function, then at test time transports behavioral samples toward high-value regions via Stein variational gradient descent, using the number of transport steps in place of a fixed regularization coefficient; under the individual-global-max (IGM) principle it proves a single-term KL bound on the joint soft-value gap that vanishes as transport converges, with an irreducible additive residual proportional to the IGM violation; empirically it achieves the best average performance across discrete and continuous offline MARL benchmarks and yields improvements in all offline-to-online configurations.