发表机构
University of Shiraz（设拉子大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对因果推断问题，提出基于深度神经网络和伪单学习者结构的创新方法来估计治疗效果异质性，在IHDP基准上与其他方法比较，用一个估计器估计两组潜在结果获可接受结果，为方法改进发展铺了路。
AI 中文摘要
因果推断已成为计算机科学、统计学、经济学、教育、医疗保健和医学等多个领域的核心问题，其广泛适用性吸引了更多研究资金和关注。近年来，由于观测数据量大且成本低于随机对照试验，从观测数据估计因果效应受到关注，因果效应估计方法的进展增强了服务个性化工具。本文提出一种估计治疗效果异质性的创新方法，其模型结构基于深度神经网络和伪单学习者。该方法在IHDP基准上与其他先进方法进行了比较，使用一个估计器估计两个治疗组的潜在结果获得了可接受的结果，为该方法的进一步发展和改进铺平了道路。
英文摘要
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.
Comments7 pages, 1 figure, 2 tables. This is the author-accepted manuscript of the paper presented at the 2nd International Conference on Artificial Intelligence and Software Engineering (AI-SOFT 2024), Shiraz University, Iran
