Sorry, there are two separate issues: the data itself (which is a big dataset where they following a big set of nurses for many years, and recorded lots of things about them), and how the data could be used to maybe get causal conclusions.
Plenty of folks at Harvard (e.g. Miguel Hernan, Jamie Robins) used this data in a sensible way to account for confounding (naturally their results are relatively low on the ‘hierarchy of evidence’, but still!) Trying to draw causal conclusions from observational data is 95% of modern causal inference!
You act like people never did a valid causal analysis of the data in the Nurses’ health study.
I know I overstated things. There are such things as natural experiments, having some causal information already, etc.
I’m not familiar with the Nurses’ health study, and a quick google only turns up its conclusions. What methods did they use?
Sorry, there are two separate issues: the data itself (which is a big dataset where they following a big set of nurses for many years, and recorded lots of things about them), and how the data could be used to maybe get causal conclusions.
Plenty of folks at Harvard (e.g. Miguel Hernan, Jamie Robins) used this data in a sensible way to account for confounding (naturally their results are relatively low on the ‘hierarchy of evidence’, but still!) Trying to draw causal conclusions from observational data is 95% of modern causal inference!