论文标题
从不完整的数据中发现因果：一种深度学习方法
Causal Discovery from Incomplete Data: A Deep Learning Approach
论文作者
论文摘要
随着系统随着人工智能的发展而变得越来越自治，从观察性感觉输入中发现因果知识很重要。通过编码事件之间的一系列因果关系，因果网络可以促进给定动作的影响并分析其潜在的数据生成机制。但是，在实际情况下，丢失的数据无处不在。直接在部分观察到的数据上直接执行现有的休闲发现算法可能导致不正确的推断。为了减轻这个问题，我们提出了一个深度学习框架，称为被指定为因果学习（ICL），以进行迭代缺失的数据插补和因果结构发现。通过对合成和真实数据的大量模拟，我们表明ICL可以在不同的数据机制下胜过最先进的方法。
As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs. By encoding a series of cause-effect relations between events, causal networks can facilitate the prediction of effects from a given action and analyze their underlying data generation mechanism. However, missing data are ubiquitous in practical scenarios. Directly performing existing casual discovery algorithms on partially observed data may lead to the incorrect inference. To alleviate this issue, we proposed a deep learning framework, dubbed Imputated Causal Learning (ICL), to perform iterative missing data imputation and causal structure discovery. Through extensive simulations on both synthetic and real data, we show that ICL can outperform state-of-the-art methods under different missing data mechanisms.
