Causal Generalization of Continuous Treatment Effects under Covariate Shift
协变量分布迁移下连续处理效应的因果推广AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究连续处理剂量响应、混杂调整和源人群到目标人群的因果效应推广。
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协变量分布迁移下连续处理效应的因果推广
- 研究问题
- 源样本具有协变量、处理和结果，而目标样本仅有协变量时，如何在协变量分布不同的人群间推广连续处理的平均剂量响应函数？
- 方法与思路
- 作者构造双样本伪结果与局部多项式回归，并扩展距离协方差最优加权，同时消除源样本处理依赖并对齐目标协变量分布。
- 材料陈述的贡献
- 摘要称，给出总体最优权重识别、近似经验最优解一致收敛及估计量渐近理论；模拟中目标剂量响应估计优于所列比较方法。
- 阅读关注点
- 应核对跨人群推广与源样本混杂控制的条件、支持重叠、权重一致收敛所需正则性，以及局部回归和权重估计误差的关系。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未展开正则条件及模拟数值，所述比较不能证明所有迁移场景中均更优。
原文摘要
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.