Regarding your first and third paragraphs: I have not asked people about West Virginia and Ohio, but my impression was that many of the problems in the midwestern state polls in 2016 arose from not adjusting for education of respondents, and that MRP on national polls using many adjustment factors did well; see here.

Regarding your second paragraph: This is a current area of research, to use modeling to better estimate the population distribution of covariates, and to fit better models of the outcome given the covariates. I don’t see why you say that “the traditional approach of linear models with deep interactions no longer is computationally feasible or statistically wise.” This traditional approach seemed to work well for Yair in analyzing the 2018 election.

]]>The path to solving these problems involve figuring out how to collect better covariates to adjust for non-response bias than currently exist and are traditionally used in the survey industry, and more importantly, figuring out how to generate ground-truth estimates for those covariates for small areas so that any kind of post-stratification is possible. Moreover on the regression side, as non-response bias becomes a bigger problem (because phone response rates are plummeting and we’re moving to opt-in samples), the number of covariates and model sophistication can grow to a point where the traditional approach of linear models with deep interactions no longer is computationally feasible or statistically wise.

Obviously the status-quo of people doing polls and then using raking to generate weights is crazy, and it’d be an improvement over the status quo for them to move to MRP. But that transition isn’t actually going to fix the current structural issues with political polling that exist right now.

]]>Pool et al. did poststratification, but did they do multilevel regression? Supposedly Tukey et al. did multilevel modeling for election modeling in 1960, but this doesnʼt seem so relevant as I donʼt think they wrote any of it up. ]]>