Did you conduct a sensitivity analysis? A new weighting‐based approach for evaluations of the average treatment effect for the treated. (2nd November 2020)

• Record Type: Journal Article
• Authors: Hong, Guanglei; Yang, Fan; Qin, Xu
• Is Part Of: Journal of the Royal Statistical Society. Volume 184:Number 1(2021)
• Page Start: 227
• Page End: 254
• Publication Date: 2020-11-02
• Subjects: bias formula -- causal inference -- confounding -- identification assumption -- propensity score -- selection bias -- sensitivity parameter; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12621
• Languages: English
• ISSNs: 0964-1998

Abstract

Abstract: In non‐experimental research, a sensitivity analysis helps determine whether a causal conclusion could be easily reversed in the presence of hidden bias. A new approach to sensitivity analysis on the basis of weighting extends and supplements propensity score weighting methods for identifying the average treatment effect for the treated (ATT). In its essence, the discrepancy between a new weight that adjusts for the omitted confounders and an initial weight that omits them captures the role of the confounders. This strategy is appealing for a number of reasons including that, regardless of how complex the data generation functions are, the number of sensitivity parameters remains small and their forms never change. A graphical display of the sensitivity parameter values facilitates a holistic assessment of the dominant potential bias. An application to the well‐known LaLonde data lays out the implementation procedure and illustrates its broad utility. The data offer a prototypical example of non‐experimental evaluations of the average impact of job training programmes for the participant population.