Regularizing Extrapolation in Causal Inference
因果推断中的外推正则化AI 解读 ↓
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因果推断中的外推正则化
- 研究问题
- 当加权估计允许负权重以改善特征平衡，却增加模型依赖和方差时，如何连续调节外推程度并评估因果结论的敏感性？
- 方法与思路
- 作者以外推惩罚替代权重非负的硬约束，推导最坏情形外推误差界，并通过优化同时控制特征不平衡、模型误设与方差。
- 材料陈述的贡献
- 摘要称，该框架提出两类偏差与方差的权衡，提供外推正则化及模型依赖敏感性分析，并展示随机试验结果向目标人群推广的应用。
- 阅读关注点
- 应核对外推程度和误差界的定义、误设范围、超参数选择，以及弱重叠下权重优化是否保持原有因果估计目标。
- 阅读边界
- 这是预印本，不能据此认定已同行评审或接收。摘要未说明误差界所依赖的函数类及调参规则，不能据此保证普遍降低风险。
原文摘要
Many common estimators in machine learning and causal inference are linear smoothers, where the prediction is a weighted average of the training outcomes. Some estimators, such as ordinary least squares and kernel ridge regression, allow for arbitrarily negative weights, which improve feature imbalance but often at the cost of increased dependence on parametric modeling assumptions and higher variance. By contrast, estimators like importance weighting and random forests (sometimes implicitly) restrict weights to be non-negative, reducing dependence on parametric modeling and variance at the cost of worse imbalance. In this paper, we propose a unified framework that directly penalizes the level of extrapolation, replacing the current practice of a hard non-negativity constraint with a soft constraint and corresponding hyperparameter. We derive a worst-case extrapolation error bound and introduce a novel "bias-bias-variance" tradeoff, encompassing biases due to feature imbalance, model misspecification, and estimator variance; this tradeoff is especially pronounced in high dimensions, particularly when positivity is poor. We then develop an optimization procedure that regularizes this bound while minimizing imbalance and outline how to use this approach as a sensitivity analysis for dependence on parametric modeling assumptions. We demonstrate the effectiveness of our approach through synthetic experiments and a real-world application, involving the generalization of randomized controlled trial estimates to a target population of interest.