Summary
In this chapter, we introduced causal pathways and conditional probability theory through a social science example, building a network-based data mining tool called Bayesian networks. We then simulated data from an educational pathway to implement Bayesian networks in Python. These tools provided a starting point for collecting additional data that could be analyzed to confirm hypotheses constructed from Bayesian networks through a class of models called SEMs. In the next chapter, we’ll pivot from causal pathways to look at another niche subfield in analytics: computational linguistics, where we will study languages and their relationships over long periods of time.
