Ghassami AmirEmad, Kiyavash Negar, Huang Biwei, Zhang Kun
Department of ECE, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
School of ISyE and ECE, Georgia Institute of Technology, Atlanta, GA, USA.
Adv Neural Inf Process Syst. 2018 Dec;31:6266-6276.
We study the problem of causal structure learning in linear systems from observational data given in multiple domains, across which the causal coefficients and/or the distribution of the exogenous noises may vary. The main tool used in our approach is the principle that in a causally sufficient system, the causal modules, as well as their included parameters, change independently across domains. We first introduce our approach for finding causal direction in a system comprising two variables and propose efficient methods for identifying causal direction. Then we generalize our methods to causal structure learning in networks of variables. Most of previous work in structure learning from multi-domain data assume that certain types of invariance are held in causal modules across domains. Our approach unifies the idea in those works and generalizes to the case that there is no such invariance across the domains. Our proposed methods are generally capable of identifying causal direction from fewer than ten domains. When the invariance property holds, two domains are generally sufficient.
我们研究从多个域中给出的观测数据来学习线性系统中因果结构的问题，在这些域中因果系数和/或外生噪声的分布可能会有所不同。我们方法中使用的主要工具是这样一个原理：在一个因果充分的系统中，因果模块及其包含的参数在不同域之间是独立变化的。我们首先介绍在一个由两个变量组成的系统中寻找因果方向的方法，并提出识别因果方向的有效方法。然后我们将这些方法推广到变量网络中的因果结构学习。以前从多域数据进行结构学习的大多数工作都假设在不同域的因果模块中存在某些类型的不变性。我们的方法统一了这些工作中的思想，并推广到不同域之间不存在这种不变性的情况。我们提出的方法通常能够从少于十个域中识别因果方向。当不变性属性成立时，通常两个域就足够了。
