Erin Hartman - Sensitivity Analysis for Survey Weights
Survey weighting allows researchers to account for bias in survey samples, due to unit nonresponse or convenience sampling, using measured demographic covariates. Unfortunately, in practice, it is impossible to know whether the estimated survey weights are sufficient to alleviate concerns about bias due to unobserved confounders or incorrect functional forms used in weighting. In the following paper, we propose two sensitivity analyses for the exclusion of important covariates: (1) a sensitivity analysis for partially observed confounders (i.e., variables measured across the survey sample, but not the target population), and (2) a sensitivity analysis for fully unobserved confounders (i.e., variables not measured in either the survey or the target population). We provide graphical and numerical summaries of the potential bias that arises from such confounders, and introduce a benchmarking approach that allows researchers to quantitatively reason about the sensitivity of their results. We demonstrate our proposed sensitivity analyses using state-level 2020 U.S. Presidential Election polls.
Erin Hartman is an Assistant Professor of Political Science at the University of California, Berkeley. Her research sits at the intersection of the social sciences and statistics. Her mission is to create a body of research that bridges these two worlds — with an emphasis on answering causal questions — within which experts from both worlds can have dialogue with one another and foster beneficial collaborations.
Her research sits primarily in the field of causal inference and survey design and analysis. Her main research agendas focus on external validity of experiments, falsification testing, and survey weighting.