Causal Generalization of Continuous Treatment Effects under Covariate Shift

• URL: http://arxiv.org/abs/2608.19383v1
• Date: Wed, 19 Aug 2026 18:56:16 GMT
• Title: Causal Generalization of Continuous Treatment Effects under Covariate Shift
• Abstract summary: Average dose-response functions are widely used to summarize causal effects of continuous treatments.<n>Most existing methods assume that the observed sample represents the target population.
• Score: 3.504408436046102
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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