Tailoring interventions to different groups
To design tailored interventions that account for heterogeneous effects, a conceptual framework incorporating both statistical and substantive considerations is essential. This framework involves identifying the sources of heterogeneity, quantifying these effects, and then designing interventions that specifically address the varied needs of different groups. Let’s discuss this more next.
Conceptual framework
Heterogeneity in treatment effects may sound simple or complex, depending on how you see it, but nevertheless, in applications, it is a challenge every causal inference scholar must address. Here’s the plan:
- Unmasking the culprits: We begin with detective work – EDA. Our goal? To identify potential suspects – demographic factors (age, gender), socioeconomic background, location, or pre-existing conditions – that might be influencing the treatment effect differently across subgroups. Advanced...
