Behavioral Data Analysis with R and Python

Chapter 1. The Causal-Behavioral Framework for Data Analysis

As we discussed in the preface, understanding what drives behaviors in order to change them is one of the key goals of applied analytics, whether in a business, a nonprofit, or a public organization. We want to figure out why someone bought something and why someone else didn’t buy it. We want to understand why someone renewed their subscription, contacted a call center instead of paying online, registered to be an organ donor, or gave to a nonprofit. Having this knowledge allows us to predict what people will do under different scenarios and helps us to determine what our organization can do to encourage them to do it again (or not). I believe that this goal is best achieved by combining data analysis with a behavioral science mindset and a causal analytics toolkit to create an integrated approach I have dubbed the “causal-behavioral framework.” In this framework, behaviors are at the top because understanding them is our ultimate goal. This understanding is achieved by using causal diagrams and data, which form the two supporting pillars of the triangle (Figure 1-1).

Figure 1-1. The causal-behavioral framework for data analysis

Over the course of the book, we’ll explore each leg of the triangle and see how they connect to each other. In the final chapter, we’ll see all of our work come together by achieving with one ...