Scrutinizing Heterogeneity in Causal Inference
In this chapter, we explore what heterogeneity means. It’s a fancy way of saying treatments don’t affect everyone equally. Some folks benefit more, some less. This matters because a one-size-fits-all approach to causality from every unit (e.g., a person or event) of the data may lead to sub-optimal results.
We’ll explore different types of heterogeneity, how to spot them in data, and how to use this knowledge to design treatments that target specific needs. Think of it like giving the right medicine to the right person.
By using powerful statistics, we can see beyond averages and understand who benefits most from treatments. This is key for creating interventions that are both effective and fair, reaching those who need them the most. Buckle up, as we’re about to make better causal decisions that reflect the real world!
In this chapter, we’ll cover the following topics:
- What is heterogeneity...
