Positivity violations can complicate estimation and interpretation of causal dose-response curves (CDRCs) for continuous interventions. Weighting-based methods are designed to handle limited overlap, but the resulting weighted targets can be hard to interpret scientifically. Modified treatment policies can be less sensitive to support limitations, yet they typically target policy-defined effects that may not align with the original dose-response question. We develop an approach that addresses limited overlap while remaining close to the scientific target of the CDRC. Our work is motivated by the CHAPAS-3 trial of HIV-positive children in Zambia and Uganda, where clinically relevant efavirenz concentration levels are not uniformly supported across covariate strata. We introduce a diagnostic, the non-overlap ratio, which quantifies, as a function of the target intervention level, the proportion of the population for whom that level is not supported given observed covariates. We also define an individualized most feasible intervention: for each child and target concentration, we retain the target when it is supported, and otherwise map it to the nearest supported concentration. The resulting feasible dose-response curve answers: if we try to set everyone to a given concentration, but it is not realistically attainable for some individuals, what outcome would be expected after shifting those individuals to their nearest attainable concentration? We propose a plug-in g-computation estimator that combines outcome regression with flexible conditional density estimation to learn supported regions and evaluate the feasible estimand. Simulations show reduced bias under positivity violations and recovery of the standard CDRC when support is adequate. An application to CHAPAS-3 yields a stable and interpretable concentration-response summary under realistic support constraints.

翻译：阳性假设违反可能使连续干预的因果剂量反应曲线估计与解释复杂化。基于加权的方法旨在处理有限重叠问题，但由此产生的加权目标可能难以进行科学解释。修正治疗策略对支持域限制的敏感性较低，但这些策略通常针对政策定义的效应，可能与原始剂量反应问题不一致。我们开发了一种在保持接近CDRC科学目标的同时处理有限重叠问题的方法。我们的研究受到赞比亚和乌干达HIV阳性儿童CHAPAS-3试验的启发，其中临床相关的依非韦伦浓度水平在协变量分层中并未得到均匀支持。我们引入了一种诊断指标——非重叠比率，该指标将目标干预水平作为函数，量化在给定观测协变量条件下该水平缺乏支持的人群比例。我们还定义了个体化最可行干预：针对每个儿童和目标浓度，当目标浓度得到支持时予以保留，否则将其映射至最近的可支持浓度。由此产生的可实现剂量反应曲线回答了以下问题：如果我们试图将所有人设定至特定浓度，但该浓度对某些个体实际上无法实现，那么将这些个体调整至其最近可达浓度后的预期结果如何？我们提出了一种插件式g-计算估计量，该估计量将结果回归与灵活的条件密度估计相结合，以学习支持区域并评估可行估计量。模拟实验显示在阳性假设违反情况下偏差减小，并在支持充分时恢复标准CDRC。对CHAPAS-3数据的应用在现实支持约束下产生了稳定且可解释的浓度反应汇总结果。