Causal Inference Using Bayesian Networks

Abstract

The availability of new computational technologies, data collection opportunities, and data size has significantly influenced the development of a data driven social scientific approach, popularly known as “computational social science”. Although a slowly growing field within social sciences and largely spearheaded by interdisciplinary scientific teams, computational social science is profoundly changing the nature social scientific analysis. Social scientists are now able to generate predictive results beyond traditional social scientific methods, thus are able to increase the power of social analysis. Machine learning classification algorithms (MLCAs) are a group of techniques utilized by present day computational social scientists. MLCAs became more user friendly for social scientists for the availability of Analytical Graphical User Interfaces (GUI). This article demonstrated the opportunities offered by data driven social scientific approach. Based on an actual research, this article explored a situation where a number of people embedded in teams were working together in a complex environment. This article first, explains the basic ideas of Bayesian Network Analysis – one set of MLCAs. Then it provides step-by-step demonstration on conducting Bayesian Network Analysis using Weka. Finally, the article demonstrates ways of interpreting and presenting social scientific results.