Predicting Census Survey Response Rates via Interpretable Nonparametric Additive Models with Structured Interactions. (arXiv:2108.11328v2 [stat.ML] UPDATED)

Accurate and interpretable prediction of survey response rates is important
from an operational standpoint. The US Census Bureau's well-known ROAM
application uses principled statistical models trained on the US Census
Planning Database data to identify hard-to-survey areas. An earlier
crowdsourcing competition revealed that an ensemble of regression trees led to
the best performance in predicting survey response rates; however, the
corresponding models could not be adopted for the intended application due to
limited interpretability. In this paper, we present new …

arxiv census interactions ml survey