# Confounding Adjustment

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.