Summary
In this chapter, you learned about the difference between correlative and causal relationships, the importance of causal modeling, and techniques such as Bayesian networks for causal inference. Later, we went through Python practices to help you start working with causal modeling and inference in your projects so that you can identify more reliable relationships between variables in your datasets and design reliable models.
In the next chapter, you will learn techniques for preserving privacy and ensuring security while maximizing the benefits of using private and proprietary data in building reliable machine learning models.
