Part 2: Causal Inference

In the first chapter of Part 2, we will deepen and strengthen our understanding of the important properties of graphical models and their connections to statistical quantities.

In Chapter 7, we’ll introduce the four-step process of causal inference that will help us translate what we’ve learned so far into code in a structured manner.

In Chapter 8, we’ll take a deeper look at important causal inference assumptions, which are critical to run unbiased causal analysis.

In the last two chapters, we’ll introduce a number of causal estimators that will allow us to estimate average and individualized causal effects.

This part comprises the following chapters:

• Chapter 6, Nodes, Edges, and Statistical (In)dependence
• Chapter 7, The Four-Step Process of Causal Inference
• Chapter 8, Causal Models – Assumptions and Challenges
• Chapter 9, Causal Inference and Machine Learning – from Matching to...