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[2602.20383] Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Summary
The paper discusses the detection and mitigation of group bias in heterogeneous treatment effects (HTEs) using a unified statistical framework, addressing implications for personalized targeting in various applications.
Why It Matters
Understanding and correcting group bias in treatment effects is crucial for accurate data interpretation and decision-making in fields like economics and machine learning. This research provides a robust framework that enhances the reliability of predictions and can lead to better-targeted interventions, ultimately improving outcomes in various sectors.
Key Takeaways
- Aggregation of predicted treatment effects can introduce systematic bias.
- A unified statistical framework is proposed for detecting and mitigating group bias.
- The framework requires minimal assumptions and is easy to implement.
- Mitigating group bias can significantly impact profit-maximizing personalized targeting.
- Empirical validation is provided through large-scale experimental data.
Statistics > Methodology arXiv:2602.20383 (stat) [Submitted on 23 Feb 2026] Title:Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects Authors:Joel Persson, Jurriën Bakker, Dennis Bohle, Stefan Feuerriegel, Florian von Wangenheim View a PDF of the paper titled Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects, by Joel Persson and 4 other authors View PDF HTML (experimental) Abstract:Heterogeneous treatment effects (HTEs) are increasingly estimated using machine learning models that produce highly personalized predictions of treatment effects. In practice, however, predicted treatment effects are rarely interpreted, reported, or audited at the individual level but, instead, are often aggregated to broader subgroups, such as demographic segments, risk strata, or markets. We show that such aggregation can induce systematic bias of the group-level causal effect: even when models for predicting the individual-level conditional average treatment effect (CATE) are correctly specified and trained on data from randomized experiments, aggregating the predicted CATEs up to the group level does not, in general, recover the corresponding group average treatment effect (GATE). We develop a unified statistical framework to detect and mitigate this form of group bias in randomized experiments. We first define group bias as the discrepancy between the model-implied and experimentally identified GATEs, derive an asymptotically normal estimator, and the...
