协变量偏移下连续处理效应的因果泛化
Causal Generalization of Continuous Treatment Effects under Covariate Shift
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中文总结 AI 辅助
针对协变量偏移场景，提出扩展DCOW的两样本局部多项式回归框架，提升了连续处理效应在目标总体中的剂量反应估计性能，且在PM2.5与心脏病死亡率关联分析中得到验证。
中文摘要 AI 辅助
平均剂量反应函数被广泛用于总结连续处理的因果效应，但大多数现有方法假设观测样本代表目标总体。本文研究协变量偏移场景：标记的源样本中观测到协变量、处理和结局，而目标样本中仅观测到协变量。我们开发基于伪结局的两样本局部多项式回归框架，利用源结局解决混杂问题，结合目标协变量定义感兴趣总体；进一步提出距离协方差最优加权（DCOW）的源到目标扩展，旨在消除源样本中处理与协变量的依赖，同时使加权源协变量分布与目标总体对齐。核心理论贡献是对该基于优化的过程进行权重层面分析：证明总体准则可识别最优源到目标权重，且在正则条件下，近似经验极小化器（含精确极小化器作为特例）一致收敛至这些权重；还建立了所得估计量的一致性和渐近正态性。模拟实验显示，与DCOW、广义倾向得分加权、熵平衡及未加权方法相比，所提方法可提升目标剂量反应估计性能。我们采用源-目标验证设计，在县一级分析PM2.5暴露与后续心脏病死亡率的关系，以说明该方法的应用。
英文摘要
Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population. We study a covariate-shift setting in which covariates, treatment, and outcome are observed in a labelled source sample, while only covariates are observed in the target sample. We develop a two-sample local polynomial regression framework based on pseudo-outcomes that use source outcomes to address confounding and target covariates to define the population of interest. We further propose a source-to-target extension of distance covariance optimal weighting (DCOW), designed to remove treatment-covariate dependence in the source sample while aligning the weighted source covariate distribution with the target population. A central theoretical contribution is a weight-level analysis of this optimization-based procedure: we show that the population criterion identifies the oracle source-to-target weights and that approximate empirical minimizers, including exact minimizers as a special case, converge uniformly to these weights under regularity conditions. We also establish consistency and asymptotic normality of the resulting estimator. Simulations show that the proposed method improves target dose-response estimation relative to DCOW, generalized-propensity-score weighting, entropy balancing, and unweighted alternatives. We illustrate the method in a county-level analysis of PM2.5 exposure and subsequent heart-disease mortality using a source-target validation design.
