面向测量误差模型的外生变量估计方法

从观察数据中发现因果关系在近年来得到了越来越多学者的关注,其中外生变量在理解因果机制中扮演者重要的角色。然而,现有的因果发现方法大多假设观察变量就是真实发生的因(果)变量,忽略了测量误差带来的影响。为此,提出了一种解决测量模型下的外生变量估计方法。通过引入triad约束,根据此约束来找出与其余所有相关成对变量都满足triad约束的变量,即外生变量。该算法不仅能够解决含有测量误差数据的估计问题,而且对于没有测量误差的数据仍然适用。实验将该算法应用于真实网络产生的数据中,结果表明,无论变量是否含有测量误差,提出方法均优于现有的其他算法。同时,基于移动基站的真实数据实验也验证了算法的有效性。

Method of estimating exogenous variables for measurement error model

Causal discovery from the observational dataset has attracted more and more research scholars’attentions in recent years,where the exogenous variable plays an important role in understanding the causal mechanism.However,most existing causal discovery algorithms assume that the observed variables are the real causal variables,and ignore the effects of measurement errors.This paper proposed a method to estimate the exogenous variables under measurement error model.Specifically,by introducing the state-of-the-art triad constraint,the variable that satisfied triad constraint with all the other related paired variables was the exogenous variable.This method could not only solve the dataset with measurement errors,but also handle with the dataset without measurement errors.Applied the algorithm to the data generated by real networks,experimental results show that the proposed method is superior to other existing algorithms regardless of whether the variables contain measurement errors.Meanwhile,the experiment of the real mobile-base-station data also verifies the effectiveness of the algorithm.