论文标题
两组变量之间的载体因果推断
Vector causal inference between two groups of variables
论文作者
论文摘要
当前识别原因效应关系的方法主要假设变量为标量随机变量。但是，在许多领域中，感兴趣的对象是向量或标量变量组。我们提出了一种新的基于约束的非参数方法，用于从观察数据中推断两个矢量值随机变量之间的因果关系。我们的方法采用了定向和无向图的稀疏性估计，基于两个新的原理，用于群体因果关系推理，我们在Pearl的基于图形模型的因果关系框架中理论上证明了理论上的合理性。我们的理论考虑是通过两个随机矢量之间因果相互作用的两种新因果发现算法补充，即使相互作用是非线性，它们在模拟中可靠地找到正确的因果方向。我们通过经验评估我们的方法，并将其与其他最先进的技术进行比较。
Methods to identify cause-effect relationships currently mostly assume the variables to be scalar random variables. However, in many fields the objects of interest are vectors or groups of scalar variables. We present a new constraint-based non-parametric approach for inferring the causal relationship between two vector-valued random variables from observational data. Our method employs sparsity estimates of directed and undirected graphs and is based on two new principles for groupwise causal reasoning that we justify theoretically in Pearl's graphical model-based causality framework. Our theoretical considerations are complemented by two new causal discovery algorithms for causal interactions between two random vectors which find the correct causal direction reliably in simulations even if interactions are nonlinear. We evaluate our methods empirically and compare them to other state-of-the-art techniques.
