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The Missing Link: Allocation Performance in Causal Machine Learning

MCML Authors

Unai Fischer Abaigar

→ Group Christoph Kern
Social Data Science and AI
→ Co-Group Frauke Kreuter

Christoph Kern

Prof. Dr.

Associate

Social Data Science and AI

Frauke Kreuter

Prof. Dr.

Principal Investigator

Social Data Science and AI

Abstract

Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations.

inproceedings

Workshop Humans, Algorithmic Decision-Making and Society @ICML 2024

Workshop Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact at the 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024.