In industry settings, we rarely have the luxury of a perfect A/B test. Markets are contaminated by competitors, local news, or seasonal shifts. This is where causal inference tools from econometrics become a "badass" superpower.
Synthetic Controls
By creating a "synthetic" version of a city using data from other similar cities, we can estimate what *would* have happened if we hadn't launched a new feature. It's as close to a crystal ball as data science gets.
I've applied this to rideshare pricing and grocery delivery logistics, finding that the "messy" data often holds the most valuable insights if you know how to look for them.