This is part of a series on Strategic Data Analytics.
Strategic data analysis (Part 1)
Strategic Data Analysis (Part 2): Descriptive Questions
→ Strategic Data Analysis (Part 3): Diagnostic Questions
Strategic Data Analysis (Part 4): Predictive Questions ← Coming soon!
Strategic Data Analysis (Part 5): Prescriptive Questions ← Coming soon!
Answering “why” questions can be difficult for any data analyst. Lack of subject matter expertise, lack of technical repertoire and lack of strategic approach can play an adverse role in helping decision makers find the right answer. However, with a solid foundation and direction, anyone can easily address these diagnostic questions.
Diagnostic questions often follow answers to descriptive questions. By asking a diagnostic question, the decision maker aims to understand how certain information came about or what caused something to happen. Therefore, when we think about diagnostic issues, we often think about causal inference. Therefore, it is good to be familiar with the general principles of causal inference.
In this article:
- Introduction to causal inference
- Strategy for answering diagnostic questions
- A case study
- Some final notes
Causal inference aims to discover how interventions (or changes to the status quo) affect outcomes. In causal inference, we assume that causality occurs when some intervention, called “a treatment,” is applied to some unit and causes a change in the outcome of that unit. If we compared the outcome of a unit with or without treatment, we could observe the effect of the treatment (i.e., causality).
For example, if we wanted to know if painting the exterior of our house before listing it for sale would make it sell faster, the most ideal scenario would require us to compare the selling time with and without painting the house simultaneously. Here, the house is our unit, painting the exterior is our treatment, and sales time is our result. However, it is impossible to paint and…
