结果选择下针对因果推断的协作表示
Collaborative representations for targeted causal inference under outcome selection
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中文总结 AI 辅助
针对结果缺失下的因果推断，提出基于协作表示的目标学习方法，通过交叉拟合伪结果学习低维表示，并交替更新目标与表示，以降低偏差并实现鲁棒一致估计。
中文摘要 AI 辅助
在结果缺失的情况下估计因果效应需要学习结果、暴露和选择模型。灵活的倾向性学习器可以很好地预测治疗或缺失性，但通过强调工具变量、弱重叠区域或与结果回归偏差无关的变异，可能会恶化目标估计。我们提出了一种基于协作表示的目标学习方法，用于在结果选择下恢复平均处理效应。该方法从交叉拟合的伪结果中学习低维暴露和选择表示，这些伪结果编码了结果回归漂移。一个有限候选库交替进行目标结果更新和表示更新，内部交叉验证使用目标感知风险选择表示复杂度、协作强度和正则化。我们给出了协作鲁棒性、一致性和在外层验证折叠目标化后的渐近线性的高层次充分条件。模拟显示有限样本偏差减少，并在不同配置下相对于竞争估计器具有明确的偏差-方差权衡。
英文摘要
Estimating causal effects with missing outcomes requires learning outcome, exposure, and selection models. Flexible propensity learners can predict treatment or missingness well while worsening target estimation by emphasizing instruments, weak-overlap regions, or variation unrelated to outcome-regression bias. We propose a collaborative representation-based targeted learning method for recovered average treatment effects under outcome selection. The method learns low-dimensional exposure and selection representations from cross-fitted pseudo-outcomes that encode outcome-regression drift. A finite candidate library alternates targeted outcome updates with representation updates, and inner cross-validation selects representation complexity, collaboration strength, and regularization using a target-aware risk. We give high-level sufficient conditions for collaborative robustness, consistency, and asymptotic linearity after outer validation-fold targeting. Simulations show finite-sample bias reduction, with explicit bias-variance trade-offs across configurations and relative to competing estimators
发表机构
- Integreat – Norwegian Centre for Knowledge-driven Machine Learning, Department of Mathematics, University of Oslo（奥斯陆大学数学系 Integreat – 挪威知识驱动机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。
