,
6 tweets,
2 min read
Read on Twitter
1/3 This is an excellent paper, that every regression analyst should read. Primarily, to appreciate how problems that have lingered in decades of confusion can be untangled today using CI tools. What I learned from it was that the "suppressor surprise" is surprising even when
2/3 cast in a purely predictive context: "How can adding a second lousy predictor make the first a better predictor?" Evidently, what people expect from predictors clashes with the logic of regression slopes. The explanation I offered here ucla.in/2N8mBMg (Section 3)
3/3 shows how the phenomenon comes about, but the reason for the clash is still puzzling: What exactly do people expect from predictors, and why? #Bookofwhy
To understand "suppressors", it is instructive to examine a "super-suppressor": A variable S that is uncorrelated with the regressor X and with outcome Y, yet, when added to the regression equation, turns X from useless to perfect predictor of Y. Puzzle: Does S exist?#Bookofwhy
Social Scientist: Look what I found! A suppressor!
Statistician: Big deal, it shows in regression analysis.
Computer Scientist: Why were you surprised?
Statistician: This is a question for psychologists.
Social Scientist: No, its a question for all scientists
Why was I surprised?
Statistician: Big deal, it shows in regression analysis.
Computer Scientist: Why were you surprised?
Statistician: This is a question for psychologists.
Social Scientist: No, its a question for all scientists
Why was I surprised?
Hate to keep you in suspense. Yes! A super-suppressor does exist! Its a variable S, uncorrelated with X and Y, that, if added to the regression, turns X from a useless to a perfect predictor of Y. Can readers guess who S is? His name will tell us what suppression is. #Bookofwhy
