Learning causality with graphs
Learning causality with graphs
复制标题
通过图表学习因果关系
DOI：
10.1002/aaai.12070
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发表时间：
2022
期刊：
影响因子：
0.9
通讯作者：
Li, Jundong
中科院分区：
文献类型：
--
作者：
Ma, Jing;Li, Jundong
近年来，基于图形数据的机器学习方法飞速发展，尤其是那些由有效的神经网络支持的方法。尽管它们在不同的真实世界场景中取得了成功，但大多数图表上的方法只关注预测或描述任务，而缺乏对因果关系的考虑。因果推理可以揭示数据内部的因果关系，促进人类对学习过程和模型预测的理解，是人工智能(AI)的重要组成部分。因果推断中的一个重要问题是因果效应估计，其目的是在个体水平(例如，每个患者)或群体水平(例如，一组患者)估计某种治疗(例如，药物处方)对结果(例如，疾病的治愈)的因果效应。本文介绍了由观测数据进行因果效应估计的背景，展望了图形化因果效应估计面临的挑战，并总结了近年来具有代表性的图形化因果效应估计方法。此外，我们还对未来相关领域的研究方向提出了一些见解。视频摘要链接：https://youtu.BE/BpDPOOqw-ns
Recent years have witnessed a rocketing growth of machine learning methods on graph data, especially those powered by effective neural networks. Despite their success in different real-world scenarios, the majority of these methods on graphs only focus on predictive or descriptive tasks, but lack consideration of causality. Causal inference can reveal the causality inside data, promote human understanding of the learning process and model prediction, and serve as a significant component of artificial intelligence (AI). An important problem in causal inference is causal effect estimation, which aims to estimate the causal effects of a certain treatment (eg, prescription of medicine) on an outcome (eg, cure of disease) at an individual level (eg, each patient) or a population level (eg, a group of patients). In this paper, we introduce the background of causal effect estimation from observational data, envision the challenges of causal effect estimation with graphs, and then summarize representative approaches of causal effect estimation with graphs in recent years. Furthermore, we provide some insights for future research directions in related area. Link to video abstract: https://youtu. be/BpDPOOqw-ns