Treatment effect heterogeneity is central to policy evaluation, social science, and precision medicine, where interventions can affect individuals differently. In observational studies, covariates, treatment, and outcomes are often only partially observed. When missingness depends on unobserved values (missing not at random; MNAR), standard methods can yield biased estimates of the conditional average treatment effect (CATE). This paper establishes nonparametric identification of the CATE under multivariate MNAR mechanisms that allow covariates, treatment, and outcomes to be MNAR. It also develops nonparametric and parametric estimators and proposes a sensitivity analysis framework for assessing robustness to violations of the missingness assumptions.

翻译：处理效应异质性在政策评估、社会科学和精准医学中至关重要，因为干预措施可能对个体产生不同影响。在观察性研究中，协变量、处理变量和结果变量往往仅被部分观测到。当缺失机制依赖于未观测值（非随机缺失；MNAR）时，标准方法可能导致条件平均处理效应（CATE）的估计产生偏差。本文建立了在多元MNAR机制下CATE的非参数识别理论，该机制允许协变量、处理变量和结果变量均为MNAR情形。同时开发了非参数与参数估计方法，并提出敏感性分析框架以评估估计结果对缺失机制假设违背的稳健性。