Observational data in political science often faces challenges from confounders, making causal inference difficult.

➡️ The Threat: We show unobserved factors significantly impact the link between oral argument quality and justice voting patterns.

➡️ Our Solution: Introducing simultaneous sensitivity analysis to assess this vulnerability directly.

➡️ How It Works: This approach quantifies how much hidden variables could sway results, offering clearer guidance on research limitations.

➡️ The Context: Using the U.S. Supreme Court as our case study—analyzing real arguments and voting outcomes from 1980–2015.

➡️ Why It Matters: Our findings suggest that many political science studies might underestimate inference threats without this rigorous assessment.

The core concept centers on a methodological innovation designed to bolster research integrity by directly confronting the pervasive issue of unobserved confounders. This approach—simultaneous sensitivity analysis—represents a novel statistical tool tailored for political scientists seeking robust causal claims from observational data.