Positivity violations pose significant challenges for causal effect estimation with observational data. Under positivity violations, available methods result in either treatment effect estimators with substantial statistical bias and variance or estimators corresponding to a modified estimand and target population that is misaligned with the original research question. To address these challenges, we propose partially retargeted balancing weights, which yield reduced estimator variance under positivity violations by modifying the target population for only a subset of covariates. Our weights can be derived under a novel relaxed positivity assumption allowing the calculation of valid balancing weights even when positivity does not hold. Our proposed weighted estimator is consistent for the original target estimand when either 1) the implied propensity score model is correct; or 2) the subset of covariates whose population is not modified contains all treatment effect modifiers. When these conditions do not hold, our estimator is consistent for a slightly modified treatment effect estimand. Furthermore, our proposed weighted estimator has reduced asymptotic variance when positivity does not hold. We evaluate our weights and corresponding estimator through applications to synthetic data, an EHR study, and when transporting an RCT treatment effect to a Midwestern population.

翻译：正性假设违反对基于观测数据的因果效应估计提出了重大挑战。在正性假设违反的情况下，现有方法要么导致处理效应估计量存在显著的统计偏差和方差，要么产生与原始研究问题不一致的修正估计量和目标总体。为解决这些挑战，我们提出了部分重定向平衡权重，该方法仅针对协变量的一个子集修改目标总体，从而在正性假设违反时降低估计量的方差。我们的权重可以在一种新颖的松弛正性假设下推导得出，该假设允许在正性不成立时仍能计算有效的平衡权重。我们提出的加权估计量在以下任一条件下对原始目标估计量具有一致性：1）隐含的倾向得分模型正确；或2）未修改其总体的协变量子集包含所有处理效应修饰因子。当这些条件不满足时，我们的估计量对轻微修正的处理效应估计量具有一致性。此外，当正性假设不成立时，我们提出的加权估计量具有更小的渐近方差。我们通过合成数据应用、一项电子健康记录研究以及将随机对照试验的处理效应迁移至中西部人群的案例，评估了我们的权重及相应估计量的性能。