An Interval Estimation Approach to Sample Selection Bias
Year: 2019
Type: preprint
Abstract: A widespread and largely unaddressed challenge in statistics is that non-random participation in study samples can bias the estimation of parameters of interest. To address this problem, we propose a computationally efficient interval estimator for a class of population parameters encompassing population means, ordinary least squares and instrumental variables estimands which makes minimal assumptions about the selection mechanism. Using results from stochastic programming, we derive valid confidence intervals and hypothesis tests based on this estimator. In addition, we demonstrate how to tighten the intervals by incorporating additional constraints based on population-level information commonly available to researchers, such as survey response rates and covariate means. We conduct a comprehensive simulation study to evaluate the finite sample performance of our estimator and conclude with a real data study on the causal effect of education on income in the highly-selected UK Biobank cohort. We are able to demonstrate that our method can produce informative bounds under relatively few population-level auxiliary constraints.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2994941197