当前位置: X-MOL 学术Entropy › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Direct and Indirect Effects—An Information Theoretic Perspective
Entropy ( IF 2.7 ) Pub Date : 2020-07-31 , DOI: 10.3390/e22080854
Gabriel Schamberg 1 , William Chapman 2 , Shang-Ping Xie 2 , Todd P Coleman 3
Affiliation  

Information theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g., neuroscience and climate science) domains. While these causal measures are desirable in that they are model agnostic and can capture non-linear interactions, they are fundamentally different from common statistical notions of causal influence in that they (1) compare distributions over the effect rather than values of the effect and (2) are defined with respect to random variables representing a cause rather than specific values of a cause. We here present IT measures of direct, indirect, and total causal effects. The proposed measures are unlike existing IT techniques in that they enable measuring causal effects that are defined with respect to specific values of a cause while still offering the flexibility and general applicability of IT techniques. We provide an identifiability result and demonstrate application of the proposed measures in estimating the causal effect of the El Niño–Southern Oscillation on temperature anomalies in the North American Pacific Northwest.

中文翻译:

直接和间接影响——信息论的视角

量化因果影响的信息论(IT)方法在理论和应用(例如神经科学和气候科学)领域的文献中都受到了一定的欢迎。虽然这些因果度量是可取的,因为它们与模型无关并且可以捕获非线性交互作用,但它们与因果影响的常见统计概念有根本的不同,因为它们(1)比较效果的分布而不是效果的值,并且( 2)是针对代表原因的随机变量而不是原因的特定值来定义的。我们在此介绍直接、间接和总体因果影响的 IT 衡量标准。所提出的措施与现有 IT 技术不同,因为它们能够测量根据原因的特定值定义的因果效应,同时仍然提供 IT 技术的灵活性和普遍适用性。我们提供了可识别性结果,并展示了所提出的措施在估计厄尔尼诺南方涛动对北美太平洋西北地区温度异常的因果影响中的应用。
更新日期:2020-07-31
down
wechat
bug