Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
异质性处理效应中的群体偏差检测与缓解AI 解读 ↓
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异质性处理效应中的群体偏差检测与缓解
- 研究问题
- 把个体条件平均处理效应预测汇总到人口或市场群体后，何时偏离实验识别的群体效应，如何检测并修正这种偏差？
- 方法与思路
- 作者以模型隐含群体效应与实验识别群体效应的差异定义偏差，构造渐近正态估计和检验，再提出具有闭式解的收缩偏差校正。
- 材料陈述的贡献
- 摘要称框架仅需样本矩即可检测并缓解群体偏差，并分析校正何时改变个性化投放决策与利润，利用大型平台实验数据展示表现。
- 阅读关注点
- 应重点核对正确设定的个体效应模型为何仍会产生群体偏差，包括分组方式、目标总体、训练估计误差及群体效应的定义。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未经核实；摘要未展开偏差形成机制、最小假设或校正风险，不能认为任何汇总必然有偏。
原文摘要
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 then provide a simple-to-implement statistical test. For mitigation, we propose a shrinkage-based bias-correction, and show that the theoretically optimal and empirically feasible solutions have closed-form expressions. The framework is fully general, imposes minimal assumptions, and only requires computing sample moments. We analyze the economic implications of mitigating detected group bias for profit-maximizing personalized targeting, thereby characterizing when bias correction alters targeting decisions and profits, and the trade-offs involved. Applications to large-scale experimental data at major digital platforms validate our theoretical results and demonstrate empirical performance.