Barren proxies
Read OriginalThe article discusses the statistical concept of 'barren proxies' in causal inference, where a measured variable is affected by a confounder but doesn't itself affect the outcome. It argues that in fields like medicine, most measured variables are technically barren proxies, but the key practical concern is whether the measurement reliably and closely tracks the true confounder under intervention, not its theoretical barrenness.
Comments
No comments yet
Be the first to share your thoughts!
Browser Extension
Get instant access to AllDevBlogs from your browser
Top of the Week
1
2
Better react-hook-form Smart Form Components
Maarten Hus
•
2 votes
3
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook
•
1 votes
4
Quoting Thariq Shihipar
Simon Willison
•
1 votes
5
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft
•
1 votes
6
Using Browser Apis In React Practical Guide
Jivbcoop
•
1 votes