为因果推断定制评分规则，提升处理效应估计精度
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
- 根据下游误差曲率设计专属评分函数
- 在多个基准上显著降低偏差与方差
- 适用于神经网络等任意模型，尤其适合因果推断
概率模型通常使用任务无关的目标函数（如对数损失）进行训练，这可能导致下游估计出现显著误差。这种差距在因果推断中的逆概率加权（IPW）中尤为严重，倾向得分接近0或1时常引发高偏差与高方差。本文提出一种原则性框架，通过匹配下游误差度量的局部曲率，推导出针对特定任务的严格恰当评分规则。以平均处理效应（ATE）估计为例，我们推导出闭式损失函数及其对应的规范概率映射，可直接集成于任意模型（如神经网络或梯度提升算法）。在多个因果推断基准上的广泛评估表明，所提目标函数始终优于标准似然和协变量平衡方法。
原文摘要 · Abstract (English)
Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ often lead to high bias and variance. We propose a principled framework for deriving task-specific strictly proper scoring rules by matching the local curvature of the downstream error metric. We apply this to the Average Treatment Effect (ATE) estimation, deriving a closed-form loss and its corresponding canonical probability mapping that can be readily integrated with any model like a neural network or a gradient boosting algorithm. Extensive evaluations on causal inference benchmarks demonstrate that our tailored objective consistently outperforms standard likelihood-based and covariate-balancing approaches.
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