Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding

no code implementations • 26 Mar 2025 • Marlies Hafer, Alexander Marx

Boeken et al. [2023] show that when training data is subject to selection, proxy variables unaffected by this process can, under certain constraints, be used to correct for selection bias to estimate $E[Y|X]$, and hence $E[Y|do(X)]$, reliably.

regression Selection bias

A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany

no code implementations • 21 Feb 2025 • Ina Dormuth, Sven Franke, Marlies Hafer, Tim Katzke, Alexander Marx, Emmanuel Müller, Daniel Neider, Markus Pauly, Jérôme Rutinowski

In this study, we examine the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information.