Precision medicine seeks to match patients with treatments that produce the greatest benefit. The Predicted Individual Treatment Effect (PITE)-the difference between predicted outcomes under treatment and control-quantifies this benefit but is difficult to estimate due to unobserved counterfactuals, high dimensionality, and complex interactions. We compared 30+ modeling strategies, including penalized and projection-based methods, flexible learners, and tree-ensembles, using a structured simulation framework varying sample size, dimensionality, multicollinearity, and interaction complexity. Performance was measured using root mean squared error (RMSE) for prediction accuracy and directional accuracy (DIR) for correctly classifying benefit versus harm. Internal validation produced optimistic estimates, whereas external validation with distributional shifts and higher-order interactions more clearly revealed model weaknesses. Penalized and projection-based approaches-ridge, lasso, elastic net, partial least squares (PLS), and principal components regression (PCR)-consistently achieved strong RMSE and DIR performance. Flexible learners excelled only under strong signals and sufficient sample sizes. Results highlight robust linear/projection defaults and the necessity of rigorous external validation.

翻译：精准医学旨在为患者匹配能产生最大获益的治疗方案。预测个体治疗效果（PITE）——即治疗与对照条件下预测结果之差——可量化此获益，但由于反事实结果不可观测、高维性及复杂的交互作用，其估计颇具挑战。我们使用一个结构化模拟框架，在改变样本量、维度、多重共线性及交互作用复杂度的条件下，比较了30余种建模策略，包括惩罚与基于投影的方法、灵活学习器以及树集成方法。性能通过均方根误差（RMSE）衡量预测准确性，通过方向准确性（DIR）衡量正确分类获益与损害的能力。内部验证产生了乐观的估计，而具有分布偏移和高阶交互作用的外部验证则更清晰地揭示了模型的弱点。惩罚与基于投影的方法——岭回归、lasso、弹性网络、偏最小二乘（PLS）和主成分回归（PCR）——始终展现出优异的RMSE和DIR性能。灵活学习器仅在信号强烈且样本量充足时表现卓越。结果凸显了稳健的线性/投影方法作为默认选择的优势，以及严格外部验证的必要性。