As someone who gave up a career in medicine in order get a doctoral degree in Causal Inference, I am half-upvoting this, because I really want it to be true :-)
I originally trained as a medical doctor, but came to the conclusion that what I was doing had almost no value on utilitarian grounds. Sure, once in a while you feel good about helping a patient, but really, if you weren’t working that day, somebody else would have done the same thing. I decided I would rather have my one-in-a-thousand chance of coming up with an original idea with real impact, instead of spending the rest of my career as a doctor, where my utilitarian impact would almost certainly be negligible.
I came to the Harvard School of Public Health intent on going into academic Global Health, but after I took an introductory course on applied causal inference with some basic DAG theory, that all changed. Partly, this was because I recognized the importance of Causal DAGs from reading Less Wrong. I ended up staying at HSPH to get a doctoral degree with some of the leading researchers in the field; this even allowed me to take a course that Ilya was a Teaching Assistant for (I ended up being a TA for the same course the following year)
Currently, my career plan is to get a faculty job at some school of public health, where I see my mission as taking part in a “reboot” of epidemiology and comparative effectiveness research, to cleanse it of the cargo cult science and magical thinking that is currently all too common, and train investigators in rigorous causal reasoning. I honestly believe that this could have a major utilitarian impact, because in the absence of randomized trials, proper causal reasoning about observational data is the only way we can learn how to make better clinical decisions that optimize patient outcomes,
( Hopefully, if I play my cards right, this career choice will also have the added benefit of giving me sufficient status in the medical community to get a real discussion started on some of the most horrific things that doctors do to patients)
Sure, once in a while you feel good about helping a patient, but really, if you weren’t working that day, somebody else
would have done the same thing.
Unfortunately this applies to most new math results as well (perhaps not on the same day, but eventually).
This is true, but I think a key difference is the time aspect. I am not really a causal inference researcher, I am more of a dragon slayer. The particular dragon I am engaging in battle is called cargo cult science . When fighting dragons, time is always essential; history will ask us how we allowed this dragon to terrorize us for so long. ( There are obviously more fiercesome creatures out there, but I don’t really have any insight on how to defeat them, so starting with cargo cult science could at least be useful as target practice )
With this particular dragon, I believe the proper strategy is to train all scientists in causal reasoning. This is analogous to telling engineers that they can build more solid bridges if they learn calculus. The earlier you get this message out, the fewer bridges collapse. And importantly, the engineers themselves don’t have to worry about the underlying mathematical theory and proofs, but it is really important that there are real mathematicians who work on that. This is why the work of people like Ilya, Pearl, Robins, Glymour, Richardson, etc is so important.
Causal inference! (I am half joking.)
As someone who gave up a career in medicine in order get a doctoral degree in Causal Inference, I am half-upvoting this, because I really want it to be true :-)
I originally trained as a medical doctor, but came to the conclusion that what I was doing had almost no value on utilitarian grounds. Sure, once in a while you feel good about helping a patient, but really, if you weren’t working that day, somebody else would have done the same thing. I decided I would rather have my one-in-a-thousand chance of coming up with an original idea with real impact, instead of spending the rest of my career as a doctor, where my utilitarian impact would almost certainly be negligible.
I came to the Harvard School of Public Health intent on going into academic Global Health, but after I took an introductory course on applied causal inference with some basic DAG theory, that all changed. Partly, this was because I recognized the importance of Causal DAGs from reading Less Wrong. I ended up staying at HSPH to get a doctoral degree with some of the leading researchers in the field; this even allowed me to take a course that Ilya was a Teaching Assistant for (I ended up being a TA for the same course the following year)
Currently, my career plan is to get a faculty job at some school of public health, where I see my mission as taking part in a “reboot” of epidemiology and comparative effectiveness research, to cleanse it of the cargo cult science and magical thinking that is currently all too common, and train investigators in rigorous causal reasoning. I honestly believe that this could have a major utilitarian impact, because in the absence of randomized trials, proper causal reasoning about observational data is the only way we can learn how to make better clinical decisions that optimize patient outcomes,
( Hopefully, if I play my cards right, this career choice will also have the added benefit of giving me sufficient status in the medical community to get a real discussion started on some of the most horrific things that doctors do to patients)
Unfortunately this applies to most new math results as well (perhaps not on the same day, but eventually).
This is true, but I think a key difference is the time aspect. I am not really a causal inference researcher, I am more of a dragon slayer. The particular dragon I am engaging in battle is called cargo cult science . When fighting dragons, time is always essential; history will ask us how we allowed this dragon to terrorize us for so long. ( There are obviously more fiercesome creatures out there, but I don’t really have any insight on how to defeat them, so starting with cargo cult science could at least be useful as target practice )
With this particular dragon, I believe the proper strategy is to train all scientists in causal reasoning. This is analogous to telling engineers that they can build more solid bridges if they learn calculus. The earlier you get this message out, the fewer bridges collapse. And importantly, the engineers themselves don’t have to worry about the underlying mathematical theory and proofs, but it is really important that there are real mathematicians who work on that. This is why the work of people like Ilya, Pearl, Robins, Glymour, Richardson, etc is so important.