QUANTIFYING CAUSAL INFLUENCES
成果类型：
Article
署名作者：
Janzing, Dominik; Balduzzi, David; Grosse-Wentrup, Moritz; Schoelkopf, Bernhard
署名单位：
Max Planck Society; Swiss Federal Institutes of Technology Domain; ETH Zurich
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/13-AOS1145
发表日期：
2013
页码：
2324-2358
关键词：
摘要：
Many methods for causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution on all these variables, the DAG contains all information about how intervening on one variable changes the distribution of the other n - 1 variables. However, quantifying the causal influence of one variable on another one remains a nontrivial question. Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy. We then introduce a communication scenario, where edges in a DAG play the role of channels that can be locally corrupted by interventions. Causal strength is then the relative entropy distance between the old and the new distribution. Many other measures of causal strength have been proposed, including average causal effect, transfer entropy, directed information, and information flow. We explain how they fail to satisfy the postulates on simple DAGs of <= 3 nodes. Finally, we investigate the behavior of our measure on time-series, supporting our claims with experiments on simulated data.