Challenges of automated machine learning on causal impact analytics for policy evaluation
Yuh-Jong Hu, Shu‐Wei Huang · 2017 2nd International Conference on Telecommunication and Networks (TEL-NET) · 2017
Automated machine learning (AutoML) refers to the full aspects of automation machine learning without human in the analytics loop. The main goals of big data analytics are to determine correlation, prediction, and cause-effect among high-dimensional data features. Until now, AutoML systems were primarily proposed for classification and regression, and lacked causal impact analytics. In this study, we address the possible challenges of extending AutoML on causal impact analytics for a policy evaluation. A simplified causal inference model has been implemented on a generic AutoML system Spark ML pipeline for a scenario of policy evaluation for (inter-)national stock market impacts analytics based on GDELT big datasets.