Temporal Causal Modelling on Large Volume Enterprise Data

Abstract

Structural Causal Modelling (SCM) with its intervention analysis is one of the promising modelling approach that assists in data driven decision making. SCM not only overcomes the black box modelling associated with most of the classification algorithms but also gives enterprises an opportunity to perform intervention analysis without having to perform randomized controlled experiments. But the large volume of enterprises' data pose challenges in learning the causal structure as existing algorithms are not suitable to learn from data present in Distributed File System (DFS). Hence algorithm presented in this paper, proposes a novel variation to PC-Stable algorithm to efficiently learn the causal structure from data present in DFS - thus enabling temporal causal modelling on large volume time-series data. The proposed learning algorithm is used to determine the causal story associated with churn in telecommunication industry and flight delay in airline industry. Our model identifies and quantifies the respective causal factors for unfavourable events churn and flight delay.