用单学习器结构提升个体治疗效应估计精度
TLRNet: Estimating Individual Treatment Effect based on Local Information and Single Learner Structure
- 基于深度网络与伪单学习器结构，统一建模处理组与对照组
- 在IHDP数据集上表现优于现有方法，仅用一个估计器完成双组预测
- 适合医疗个性化决策等需精准因果推断的场景
因果推断已成为计算机科学、统计学、经济学、教育、医疗等多个领域的核心问题，其广泛应用吸引了越来越多的研究投入。近年来，由于观测数据量庞大且成本远低于随机对照试验，从观测数据中估计因果效应成为研究热点。因果效应估计方法的进步推动了服务个性化工具的发展，例如可为每位患者识别出最有效的治疗方案（兼顾成本与成功率）。本文提出一种新颖的异质性治疗效应估计方法，模型结构基于深度神经网络和伪单学习器。该方法在IHDP基准数据集上与当前先进方法进行了对比，仅使用一个估计器即实现了对两组潜在结果的估计，取得了可接受的效果，为后续改进提供了基础。
原文摘要 · Abstract (English)
Causal inference has become a central issue across various fields, including computer science, statistics, economics, education, healthcare, and medicine. The broad applicability of this discipline has garnered increased research funding and attention. In recent years, the estimation of causal effects from observational data has gained traction due to the vast amounts of collected data and the lower costs compared to randomized controlled trials. Advances in causal effect estimation methods have enhanced service personalization tools. For instance, these tools can help identify the most effective type of treatment (considering both cost and success rate) for each patient among different medical service options. This paper proposes an innovative method for estimating the heterogeneity of treatment effects. The structure of the proposed model is based on a deep neural network and a pseudo-single learner. The proposed method has been compared with other state-of-the-art methods on the IHDP benchmark. Acceptable results have been obtained by using one estimator to estimate the potential outcomes of two treatment groups. Accordingly, this paves the way for further development and improvement of the proposed method.
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