This paper questions sensitivity analysis reliability.

Data & Methods

➡️ Using Monte Carlo simulations, we assess the robustness of variables across different data-generating processes.

Key Findings

➡️ Determinants and confounders' correlation significantly impact inference validity.

➡️ Variables with strong outcome influence are more likely to be correctly identified as robust.

➡️ Leamer's extreme bounds analysis and Bayesian model averaging reduce false positives compared to other methods.

➡️ Sensitivity test results depend heavily on the specific definition of robustness chosen.

Why It Matters

➡️ Researchers should focus on variables meeting inferential criteria rather than relying solely on sensitivity tests.