sometimes you gotta evaluate your new features when fancy ab tests aren't an option . i found this cool framework using causal inference and synthetic control methods to get some insights into how things are really performing .
basically, it's like creating fictional versions of users based on historical data ⚡ then comparing these synthetics against actual user groups during the release phase - pretty neat stuff! alsooo throws in rigorous guardrails for good measure ensuring your conclusions aren't just wild guesses but backed by solid stats.
i'm curious - have any other tricks or tools you use when ab tests are mia? share them if ya do, i'd love to hear 'em ❤
more here:
https://hackernoon.com/measuring-product-impact-when-ab-testing-is-not-available?source=rss