<filename>article/sections/_0_abstract.tex
\section*{Abstract}

In this chapter, we focus on using causal graphs to make causal inferences in choice modelling contexts.
We address a longstanding disconnect whereby choice modellers have not adopted techniques from causal inference researchers, even when trying to make causal inferences.
To bridge this disconnect, we conduct simulation studies to first demonstrate the need for paying close attention to causal graphs in choice modelling.
We then present new guidelines, methods, and perspectives for the construction and validation of causal graphs, complete with empirical examples.
Next we give examples and direction for using one's causal graphs to make causal inferences in common choice modelling scenarios, including those with latent confounding.
Finally, we also provide extensive literature reviews of testing and discovery methods for causal graphs.
At the chapter's conclusion, choice modellers should have a much clearer understanding of (or references to find out about) why they should use causal graphs, what graphs to use for their dataset, and how they can use a given causal graph to complete their causal inferences.
