Using Instrumental Variables to Evaluate LLM Routing Decisions in Python
EDITOR BRIEF
The excerpt explains that in multi-model gateways, simple regression can give misleading results because routing choices are tied to query difficulty. A premium-vs-cheaper model comparison can mix the model’s effect with the fact that harder queries are routed differently. It introduces instrumental variable analysis as a way to separate these effects using a third factor that changes routing without being related to query quality.
INSIGHTS
This matters because beginners often trust correlation as if it were cause and effect. A good next step is to learn the basics of causal inference and instrumental variables, then try a small Python example that compares naïve regression with a more careful causal estimate.
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