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.
| Assessing Threats to Inference with Simultaneous Sensitivity Analysis: The Case of U.S. Supreme Court Oral Arguments was authored by Daniel Lempert and Jeffrey Budziak. It was published by Cambridge in PSR&M in 2018. |
