Causal Generalization of Continuous Treatment Effects under Covariate Shift

2026-08-19

AI Summary

研究在协变量偏移下连续治疗效应的因果泛化问题，提出基于伪结果的两样本局部多项式回归框架及距离协方差最优加权方法来解决。

Abstract

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.

Problem

• Covariate Shift
• Continuous Treatment Effects
• Dose-Response Function
• Causal Generalization

Innovation

• local polynomial regression
• pseudo-outcomes
• distance covariance optimal weighting (DCOW)
• covariate shift
• causal effects