Constructing Causality Models with Graphs
In the previous chapters, we really got our hands dirty with some complex stuff – causality, confounding, association, and how they play out in statistics. We didn’t just talk theory; we got practical, diving into sample regression and propensity score matching, mostly using R, to see how these concepts come alive in real research. Now, we’ll head into the world of graphical models that explain causal relationships in a different way.
This chapter mixes graph theory – which is all about dots and lines and how they connect – with our focus on figuring out cause and effect. Our goal? To give you a toolkit that makes sense of causal connections in various situations. We’ll start with graph theory basics and gradually get into the nitty-gritty of dynamic causal models.
The topics covered in this chapter include the following:
- Basics of graph theory
- Graph representations of variables ...
