ABSTRACT
The history of causal inference is long and complex, with many thinkers from a variety of disciplines having written on the topic. In this chapter, we briefly touch upon some prominent milestones in the field, including contributions by Aristotle, Hume, Lind, Mill, Neyman, Wright, and Hill. Next, we define confounding and then introduce some of the data examples used to illustrate different methods of confounding adjustment and other procedures in the text, including mortality rates in the US and China, admissions data for US colleges and universities, the What-If? clinical trial to reduce alcohol consumption, the simulated Double What-If? Study (What If the What-If study had confirmed theory?) together with the simulation R code, data from the General Social Survey, and a pediatric cancer clinical trial.
