A Framework for Crowd-Based Causal Analysis of Open Data
Jing Song, Satoshi Oyama, Masahito Kurihara · 2018
Many organizations provide open data, and important insights can be gained by analyzing such data. Analysis of the potential causal relationships is a complex task. We have developed a framework for analyzing causal relationships that combines the intelligence of the crowd with state-of-the-art machine learning methods. The proposed framework takes into account the effect of possible confounding in causal analysis by collecting explanations of the correlation between variables. The validity of the collected explanations is tested using a causal discovery workflow including a conditional independence test step and a causal direction inference step. Application of this framework to data obtained from the World Bank Data website and open government data sources revealed several interesting causal relationships. The results demonstrate that the proposed framework can efficiently perform causal analysis of open data.