The Missing Link: Allocation Performance in Causal Machine Learning
This addresses the limited research on how causal ML challenges impact downstream decision-making tasks, such as in social welfare and healthcare, but it is incremental as it highlights existing issues without proposing new solutions.
The paper tackles the problem of how causal machine learning models perform in automated decision-making systems, using a real-world jobseeker dataset to show that a single CATE model's performance varies significantly across different decision-making scenarios and is affected by challenges like distribution shifts.
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.