Assessing the Unobserved: Enhancing Causal Inference in Sociology with Sensitivity Analysis

Explaining social events is a primary objective of applied data-driven sociology. To achieve that objective, many sociologists use statistical causal inference to identify causality using observational studies research context where the analyst does not control the data generating process. However, it is often challenging in observation studies to satisfy the unmeasured confounding assumption, namely, that there is no lurking third variable affecting the causal relationship of interest. In this article, we develop a framework enabling sociologists to employ a different strategy to enhance the quality of observational studies. Our framework builds on a surprisingly simple statistical approach, sensitivity analysis: a thought-experimental framework where the analyst imagines a lever, which they can pull for probing a variety of theoretically driven statistical magnitudes of posited unmeasured confounding which in turn distorts the causal effect of interest. By pulling that lever, the analyst can identify how strong an unmeasured confounder must be to wash away the estimated causal effect. Although each sensitivity analysis method requires its own assumptions, this sort of post-hoc analysis provides underutilized tools to bound causal quantities. Extending Lundberg et al, we develop a five-step approach to how applied sociological research can incorporate sensitivity analysis, empowering scholars to rejuvenate causal inference in observational studies.