Free Lesson
Causal Experimentation 101: Prove Impact Without an A/B Test
Part of Build Your AI Product Analyst
45 min
Oct 28, 2026 1:00 PM
What you'll learn
Use pre-post, diff-in-diff, and matching
The three go-to methods for estimating impact when a clean randomized test was never an option.
Know which method fits your situation
A simple decision path from what happened and what data you have to the method that will actually hold up.
Spot the confounders that fool you
The seasonality, selection, and trend traps that make a change look like it worked when it did not.
Make a defensible causal claim
State the effect, the assumptions behind it, and the caveats, so it survives the first hard question.
Why this topic matters
Most impactful questions never get a clean A/B test. The feature shipped, the change hit everyone at once, or the sample is too small. Pre-post, diff-in-diff, and matching let you estimate real impact from your data, and AI makes running them fast. The hard part is picking the right method and catching the confounders that break your claim. This is the no-experiment causal toolbox, made simple.
