Risk-Calibrated Balancing for High-Dimensional Causal Extrapolation
高维因果外推中的风险校准平衡AI 解读 ↓
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高维因果外推中的风险校准平衡
- 研究问题
- 在高维弱重叠环境下，更强协变量平衡可能集中权重并增加方差，如何按反事实均值的估计风险选择平衡强度？
- 方法与思路
- 作者对归一化基础权重实施岭增广，依据反事实均值的条件预测风险调参，并在随机效应模型和比例渐近框架下分析风险。
- 材料陈述的贡献
- 摘要称，方法提供失衡与权重方差的有限样本风险分解，并为自适应权重构造一致的目标风险估计，使缩放后的超额风险趋于零。
- 阅读关注点
- 应核对随机效应预测模型、基础权重的设计独立性与自适应情形的区别，以及风险估计、缩放方式和处理效应推断之间的关系。
- 阅读边界
- 所据为 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明全部风险模型条件，应用改善不能外推至任意弱重叠情形。
原文摘要
In observational causal inference, covariate balancing is widely used to reduce source-target covariate shift, but under weak overlap in high dimensions, stronger balance can induce concentrated weights and increase variance. Balance measures how well the target covariate distribution is represented, but does not by itself determine how reliably the counterfactual mean can be estimated. We develop risk-calibrated balancing for the average treatment effect on the treated, which applies ridge augmentation to any normalised base weights and selects its penalty using conditional prediction risk of the counterfactual mean. Under a random-effects predictive model, we derive an exact finite-sample decomposition of this risk into residual covariate imbalance and weight-induced variance. For design-independent base weights under proportional asymptotics, we characterise how limiting risk depends on source and target covariance geometry, population mean shift, and weight concentration. For covariate-adaptive base weights, we develop a uniformly consistent target-aware risk estimator whose minimiser attains vanishing scaled oracle excess risk. Simulations show that the high-dimensional risk predictions remain informative for adaptive balancing and that target-aware tuning generally reduces excess target risk. Empirical analyses of job-training and single-cell perturbation data show that risk-calibrated balancing generally improves on the corresponding base estimators, with larger gains under weaker overlap.