用深度学习建模多种治疗策略的异质效应，实现精准排序与优化。
Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effects
- 基于神经增强朴素贝叶斯层，融合连续与离散治疗因子。
- 在真实数据集上显著提升聚合异质效应的预测性能。
- 适合医疗决策、个性化干预等需要多维策略评估的场景。
随着异质治疗效应（HTE）在科学与工业领域的广泛应用，治疗动作空间自然扩展，从二元变量发展为包含连续强度或离散分配的结构化策略。本文从基础原理出发，推导了将多个治疗策略变量纳入个体与平均治疗效应函数形式的方法。在此基础上，提出一种直接利用聚合HTE函数对受试者进行排序的算法。特别地，在深度学习框架中构建神经增强朴素贝叶斯层，可处理任意数量满足朴素贝叶斯假设的策略因子。该因子层被应用于连续治疗变量、治疗分配及聚合效应函数的直接排序。整体算法形成一个通用的异质治疗策略深度学习框架，并在公开数据集上验证其性能优势。
原文摘要 · Abstract (English)
As estimation of Heterogeneous Treatment Effect (HTE) is increasingly adopted across a wide range of scientific and industrial applications, the treatment action space can naturally expand, from a binary treatment variable to a structured treatment policy. This policy may include several policy factors such as a continuous treatment intensity variable, or discrete treatment assignments. From first principles, we derive the formulation for incorporating multiple treatment policy variables into the functional forms of individual and average treatment effects. Building on this, we develop a methodology to directly rank subjects using aggregated HTE functions. In particular, we construct a Neural-Augmented Naive Bayes layer within a deep learning framework to incorporate an arbitrary number of factors that satisfies the Naive Bayes assumption. The factored layer is then applied with continuous treatment variables, treatment assignment, and direct ranking of aggregated treatment effect functions. Together, these algorithms build towards a generic framework for deep learning of heterogeneous treatment policies, and we show their power to improve performance with public datasets.
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