标题： 纵向数据中高维混杂因素的异质分位数处理效应估计 显示英文标题

标题： Heterogeneous Quantile Treatment Effect Estimation for Longitudinal Data with High-Dimensional Confounding

摘要： 因果推断在各种实际应用中起着基础性作用。 然而，在具有启发性的非小细胞肺癌（NSCLC）研究中，估计化疗对循环肿瘤DNA（ctDNA）的治疗效果具有挑战性。 首先，异质性治疗效果在由基线特征定义的患者亚组中有所不同。 其次，存在广泛的人口统计学、临床和分子变量作为潜在混杂因素。 第三，随时间变化的ctDNA轨迹表现出重尾非高斯行为。 最后，受试者内的重复测量引入了未知的相关性。 结合卷积平滑分位数回归和正交随机森林，我们提出了一种框架，在高维混杂因素存在的情况下估计异质分位数治疗效应，该框架不仅能够捕捉协变量之间的效应异质性，而且对干扰参数估计误差具有鲁棒性。 我们建立了所提出的估计量的理论性质，并通过全面的模拟实验展示了其有限样本性能。 我们在具有启发性的NSCLC研究中展示了其实际应用价值。 显示英文摘要

摘要： Causal inference plays a fundamental role in various real-world applications. However, in the motivating non-small cell lung cancer (NSCLC) study, it is challenging to estimate the treatment effect of chemotherapy on circulating tumor DNA (ctDNA). First, the heterogeneous treatment effects vary across patient subgroups defined by baseline characteristics. Second, there exists a broad set of demographic, clinical and molecular variables act as potential confounders. Third, ctDNA trajectories over time show heavy-tailed non-Gaussian behavior. Finally, repeated measurements within subjects introduce unknown correlation. Combining convolution-smoothed quantile regression and orthogonal random forest, we propose a framework to estimate heterogeneous quantile treatment effects in the presence of high-dimensional confounding, which not only captures effect heterogeneity across covariates, but also behaves robustly to nuisance parameter estimation error. We establish the theoretical properties of the proposed estimator and demonstrate its finite-sample performance through comprehensive simulations. We illustrate its practical utility in the motivated NSCLC study.