Kun Kuang

Yunzhe Li

Bo Li

Peng Cui

Hongxia Yang

Jianrong Tao

Fei Wu

Data Mining and Knowledge Discovery

数据收集、存储和分发方面的进步已经产生了对帮助数据分析的计算工具和技术的需求。数据库中的数据挖掘和知识发现 (KDD) 是一个快速发展的研究和应用领域，它建立在许多领域的技术和理论之上，包括统计学、数据库、模式识别和学习、数据可视化、不确定性建模、数据仓库和 OLAP，优化和高性能计算。

With advancements in data collection, storage, and distribution technologies, there is a growing demand for tools and techniques that assist in data analysis. Data Mining and Knowledge Discovery (KDD) is a rapidly evolving field of research and application that builds upon technologies and theories from various disciplines, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modeling, data warehousing and OLAP, optimization, and high-performance computing.

随着数据收集、存储和分发技术的进步，市场对数据分析工具和技术的需求日益增长。数据挖掘与知识发现（Data Mining and Knowledge Discovery，KDD）是一个快速发展的研究与应用领域，融合了统计学、数据库、模式识别与学习、数据可视化、不确定性建模、数据仓库与OLAP、优化以及高性能计算等多个学科的技术和理论。

Continuous treatment effect estimation via generative adversarial de-confounding

It is shown, both theoretically and with empirical experiments, that the GAD algorithm can remove the associations between covariates and treatment, hence, precisely estimating the causal effect of continuous treatment.

Category	Quartile
计算机科学	3
计算机科学, 计算机人工智能	3
计算机科学, 计算机信息系统	3

Continuous treatment effect estimation via generative adversarial de-confounding

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	2