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Deep dive into the approach for answering “why” questions
This is part of a series on Strategic Data Analysis.
→ 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, these diagnostic questions can be easily tackled by anyone.
Diagnostic questions frequently follow the answers to descriptive questions. In asking a diagnostic question, the decision maker aims to understand how some piece of information came about or what caused something to happen. Thus, when we think about diagnostic questions, 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
- A Few Final Notes
Causal inference aims to uncover how interventions (or changes in status quo) effect outcomes. In causal inference, we suppose that causality happens when some intervention, called “a treatment”, is applied to some unit and it causes a change in that unit’s outcome. If we were to compare an outcome of a unit with or without the treatment, we would be able to observe the effect of the treatment (i.e. causality).
For example, if we wanted to know if painting our house exterior prior to listing it for sale would make it sell faster, the most ideal scenario would require us to compare time-to-sale with and without painting the house simultaneously. Here, the house is our unit, painting the exterior is our treatment, and time-to-sale is our outcome. However, it is impossible to both paint and…
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