Local sensitivity approximations for selectivity bias

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2001 · Volume 63 · Issue 4

Abstract

Observational data analysis is often based on tacit assumptions of ignorability or randomness. The paper develops a general approach to local sensitivity analysis for selectivity bias, which aims to study the sensitivity of inference to small departures from such assumptions. IfMis a model assuming ignorability, we surroundMby a small neighbourhoodNdefined in the sense of Kullback–Leibler divergence and then compare the inference for models inNwith that forM. Interpretable bounds for such differences are developed. Applications to missing data and to observational comparisons are discussed. Local approximations to sensitivity analysis are model robust and can be applied to a wide range of statistical problems.

Keywords

Ignorability, Missing data, Selection bias, Sensitivity analysis

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