Perhaps for a simpler example: imagine you start an ad campaign on the same exact day the government issues a huge stimulus check. You see a big increase of sal

Perhaps for a simpler example: imagine you start an ad campaign on the same exact day the government issues a huge stimulus check. You see a big increase of sales. Is it because the ad did a good job of causally impacting people to choose your brand? Or is it because the world changed underneath your feet and suddenly lots of people with pre-existing demand had more money? Maybe your ad even had a negative effect because more people were willing to spend the stimulus check on your product, but they were turned off by the ad - so your revenue went way up but the ad actually lost you money. This is about the simplest type of confounding example you’ll see - real world ad attribution is far messier, with 10 or 15 of these issues all intermixing, some of which you have measured data to possibly use as a control, some of which you don’t. Your client might not even have done any control group at all. They might just say, “we sent this ad to absolutely everyone, how did it perform compared to a hypothetical where we didn’t send it at all?”