Navigating Unmeasured Confounding in Quantitative Sociology: A Sensitivity Framework

TL;DR

This paper introduces a sensitivity analysis framework for assessing the impact of unmeasured confounders in causal inference within sociology, emphasizing integration into research design for improved transparency.

Contribution

It extends existing sensitivity analysis methods and proposes a five-step workflow to incorporate these techniques into research design, enhancing causal inference rigor in sociology.

Findings

Different sensitivity methods offer complementary insights.

Sensitivity analysis improves causal transparency.

Application to empirical data demonstrates practical utility.

Abstract

Unmeasured confounding remains a critical challenge in causal inference for the social sciences. This paper proposes a sensitivity analysis framework to systematically evaluate how unmeasured confounders influence statistical inference in sociology. Given these sensitivity analysis methods, we introduce a five-step workflow that integrates sensitivity analysis into research design rather than treating it as a post-hoc robustness check. Using the Blau and Duncan (1967) study as an empirical example, we demonstrate how different sensitivity methods provide complementary insights. By extending existing frameworks, we show how sensitivity analysis enhances causal transparency, offering a practical tool for assessing uncertainty in observational research. Our approach contributes to a more rigorous application of causal inference in sociology, bridging gaps between theory, identification strategies, and statistical modeling.