🤖 AI Summary
Unmeasured confounding poses a persistent threat to causal inference in social science research. This paper introduces a comprehensive sensitivity analysis framework tailored for sociological empirical studies, embedding sensitivity assessment directly into a five-step research design workflow—rather than treating it as a post-hoc check. The framework innovatively integrates E-values, R²-based sensitivity metrics, counterfactual simulation-based perturbation analysis, and the Blau–Duncan path model to unify identification strategy, statistical modeling, and causal transparency. Applied to classic intergenerational mobility research, it demonstrates methodological complementarity across approaches, substantially enhancing the robustness, credibility, and interpretability of causal conclusions drawn from observational data. By providing a systematic, operationally feasible sensitivity analysis paradigm, the framework advances rigorous causal inference in sociology.
📝 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.
