a few years ago I worked on an ML powered application that was distributing tv ad money across networks and programs. TV ads dont have the benefit of clickthrough data. instead, we used correlations like: people who watch Friends buy Nissan SUVs and shop at natural markets. they tend to go on roadtrips instead of taking the plane.. and the like. I'd try to use to correlate voter data with lifestyle preferences and from there figure out which slices are missing from poll samples.
Have folks tried to do backwards looking polls to try and quantify the selection bias? One could imagine a very simple experiment where you take a poll of known voters during '16 (so that you don't have to deal with flawed turnout estimates) and try and recover the precinct level or county level vote shares. From there you should be able to get a nonresponse estimate, assuming you mitigate partisan social desirability bias (using randomization or some other technique).
What 2020 taught us about polarization and the polls 📊 November 15, 2020
a few years ago I worked on an ML powered application that was distributing tv ad money across networks and programs. TV ads dont have the benefit of clickthrough data. instead, we used correlations like: people who watch Friends buy Nissan SUVs and shop at natural markets. they tend to go on roadtrips instead of taking the plane.. and the like. I'd try to use to correlate voter data with lifestyle preferences and from there figure out which slices are missing from poll samples.
Have folks tried to do backwards looking polls to try and quantify the selection bias? One could imagine a very simple experiment where you take a poll of known voters during '16 (so that you don't have to deal with flawed turnout estimates) and try and recover the precinct level or county level vote shares. From there you should be able to get a nonresponse estimate, assuming you mitigate partisan social desirability bias (using randomization or some other technique).