= 27d567bc2d48d9f40e9ccc20ea370b15)
Kawoomba comments on Causal Diagrams and Causal Models - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (274)
Thanks!
This seems like a big problem for inferring "no causation" from "no correlation." Is there a standard methodological solution? And, do researchers often just choose to infer "no causation" from "no correlation" and hope for the best, or do they avoid inferring "no causation" from "no correlation" due to the fact that they can't tell whether the faithfulness assumption holds?
"No subtle confounders" and "increasing sample size (decreases relevance and likelihood of such special cases)" would have m-answered your previous z-comments. (SCNR)