In Opinion
The system that turned Gila monster venom into GLP-1 drugs is being dismantled through research budget cuts, Jeff Coller writes in a guest essay. “Less support for scientists means strange questions no one will get to chase. Exploring those questions is how medicine advances.”
I think Christmas Carol and Treasure Island were published as books 40 years apart. Add 40 years to Treasure Island publication date and you’ve got 1923.
So I’m pulling for a muppet adaptation of Gibran’s The Prophet.
There is no other choice. Clearly.
Anyway, times like these I wish I finished the ole PhD and a larger citation record cuz I am pretty sure I’m right and my argument is fairly sound but I don’t have the authority here, which is sometimes what matters.
But it is generally a good idea if authors are explicit in their discussion of results about their assertion/interpretation that some quantitative relational estimand represents a causal relationship, even if that interpretation is bad right?
Science turned a serendipitous finding about lizard venom into one of the most important drugs of the century, but that type of research is getting harder to do.
I just want people to say what they mean so that people can scrutinize explicit claims against evidence.
I have not written a peer-reviewed journal article for quite some time, but I do read a whole lot of them and have in some cases applied their results to help companies either make or save a lot of actual money. That said, having not done the thing for a long time, maybe I’m missing something.
I furthermore think people should always be explicit about whether and how an estimate represents a causal relationship, and what that interpretation tells us about the state of the world, causal or predictive or descriptive.
What I mean is: would you prefer authors are explicit in their discussion of the results that they think the relationship is causal, rather than use obfuscatory language in the paper and then causal language in, say, podcast interviews and pop science books?
I as a reader of papers who uses results to do applied research that makes or saves companies money would personally like that to happen more, but then again, I’m not a real scientist maybe.
Have @dingdingpeng.the100.ci and/or @vincentab.bsky.social and company written a paper yet about the models as the prediction machines approach applied to causal inference that looks at the issues of regularization bias, and/or unit-varying effects? (Please do)