Causal reasoning is at the core of everything we see, do, and imagine. Causal inference is the foundation of scientific thinking and reasoning. Every explicit decision we make is the realization of causal thinking. You will be surprised to learn that the rigorous study of causality as a science is relatively new in comparison to the disciplines of statistics and probability. The history of the Causal Revolution will surprise, inspire, entertain, and—at times—shock you!
In Part 1 we introduced directed acyclic graphs (DAGs)  as a better way to represent program theory . A DAG is a Bayesian network where each directed arrow represents a causal link, not merely a probabilistic dependency .
Program theory has three components:
Theory of causation (causal model) Theory of change (selected strategy) Theory of action (selected intervention that activates change) Program theory is often called “theory of change.
Every public health intervention has a program theory; however, very few can actually describe the program theory supporting their primary programmatic activity or research. Can you? If not, read on.
I too could not describe the program theories supporting my own work until I read Funnell Rogers’ book Purposeful Program Theory . It turns out that program evaluators not only live and breath program theory, but they call it by different names: logic model, program logic, theory of change, causal model, results chain, intervention logic, etc.
Several years ago I (TJA) developed the ‘epitools’ R package for epidemiologic data and graphics. My goal was to have a practical package for practicing epidemiologists at local and state health departments. It was designed to conduct contingency table analyses for outbreak investigations, to construct epidemic curves, to improve graphical color selection, to implement basic methods from Chapter 4 of Rothman’s Modern Epidemiology, and more.
Recently, ‘epitools’ maintenance was taken over by Adam Omidpanah from the Washington State University.