Examining a Layered Approach for Efficient Causal Discovery based on Web Page Visit History

Abstract

This research introduces a unique approach to examining causal relationships amongst attributes of specific customer groups who conducted contract procedures on web pages using causal discovery. The aim here is to lessen the processing burden during causal exploration and uphold accuracy. To tackle this challenge, we structure customer groups into tiers, depending on their web page trajectories, take out correlated attributes, and suggest a process for causal exploration within each tier. The experiment's outcomes substantiate that this strategy mitigates the processing load by lessening the quantity of attributes processed concurrently, while also generating a graph signifying causal relationships among attributes. This method offers an efficient strategy for scrutinizing causal relationships in customer groups relying on their web page visit history.