基于偏好关系建模治疗效果差异，提升个性化决策准确性。
Preference-based Conditional Treatment Effects and Policy Learning
- 以偏好排序替代具体结果值，灵活处理多维、有序或偏好驱动数据
- 在不可识别条件下仍给出可解释估计目标，突破传统方法局限
- 适用于医疗、金融等需个性化决策的场景，尤其适合结果难量化时
我们提出一种基于偏好的条件治疗效应估计与策略学习新框架，核心为条件偏好型治疗效应（CPTE）。该方法仅要求结果可通过偏好规则排序，即可灵活建模异质性效应，适用于条件必要性/充分性概率、条件胜率比及广义成对比较等场景。尽管基于比较的估计量存在固有不可识别性，CPTE仍提供可解释的目标，并为此前不可识别的量提供了新的可识别条件。我们通过匹配、分位数与分布回归等策略实现估计，并设计高效影响函数估计器以修正插补偏差并最大化策略价值。合成与半合成实验表明，该方法性能显著提升，具有实际应用价值。
原文摘要 · Abstract (English)
We introduce a new preference-based framework for conditional treatment effect estimation and policy learning, built on the Conditional Preference-based Treatment Effect (CPTE). CPTE requires only that outcomes be ranked under a preference rule, unlocking flexible modeling of heterogeneous effects with multivariate, ordinal, or preference-driven outcomes. This unifies applications such as conditional probability of necessity and sufficiency, conditional Win Ratio, and Generalized Pairwise Comparisons. Despite the intrinsic non-identifiability of comparison-based estimands, CPTE provides interpretable targets and delivers new identifiability conditions for previous unidentifiable estimands. We present estimation strategies via matching, quantile, and distributional regression, and further design efficient influence-function estimators to correct plug-in bias and maximize policy value. Synthetic and semi-synthetic experiments demonstrate clear performance gains and practical impact.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。