Tailoring treatments to individual needs is a central goal in fields such as medicine. A key step toward this goal is estimating Heterogeneous Treatment Effects (HTE) - the way treatments impact different subgroups. While crucial, HTE estimation is challenging with survival data, where time until an event (e.g., death) is key. Existing methods often assume complete observation, an assumption violated in survival data due to right-censoring, leading to bias and inefficiency. Cui et al. (2023) proposed a doubly-robust method for HTE estimation in survival data under no hidden confounders, combining a causal survival forest with an augmented inverse-censoring weighting estimator. However, we find it struggles under heavy censoring, which is common in rare-outcome problems such as Amyotrophic lateral sclerosis (ALS). Moreover, most current methods cannot handle instrumental variables, which are a crucial tool in the causal inference arsenal. We introduce Multiple Imputation for Survival Treatment Response (MISTR), a novel, general, and non-parametric method for estimating HTE in survival data. MISTR uses recursively imputed survival trees to handle censoring without directly modeling the censoring mechanism. Through extensive simulations and analysis of two real-world datasets-the AIDS Clinical Trials Group Protocol 175 and the Illinois unemployment dataset we show that MISTR outperforms prior methods under heavy censoring in the no-hidden-confounders setting, and extends to the instrumental variable setting. To our knowledge, MISTR is the first non-parametric approach for HTE estimation with unobserved confounders via instrumental variables.

翻译：根据个体需求定制治疗方案是医学等领域的核心目标。实现这一目标的关键步骤是估计异质性处理效应（HTE）——即治疗对不同亚组的影响方式。尽管至关重要，但在生存数据中估计HTE具有挑战性，因为事件（例如死亡）发生的时间是关键信息。现有方法通常假设数据被完全观测，这一假设在生存数据中因右删失的存在而被违背，从而导致估计偏倚和效率低下。Cui等人（2023）提出了一种在无隐藏混杂因子条件下估计生存数据HTE的双稳健方法，该方法将因果生存森林与增强的逆删失加权估计器相结合。然而，我们发现该方法在严重删失情况下表现不佳，而这在诸如肌萎缩侧索硬化症（ALS）等罕见结局问题中十分常见。此外，当前大多数方法无法处理工具变量，而工具变量是因果推断工具箱中的关键工具。我们引入了生存治疗反应多重插补法（MISTR），这是一种新颖、通用且非参数化的方法，用于估计生存数据中的HTE。MISTR使用递归插补的生存树来处理删失，而无需直接对删失机制进行建模。通过对两个真实世界数据集——艾滋病临床试验组协议175和伊利诺伊州失业数据集——的广泛模拟分析，我们表明，在无隐藏混杂因子设置下，MISTR在严重删失情况下优于先前方法，并且可扩展到工具变量设置。据我们所知，MISTR是首个通过工具变量处理未观测混杂因子的非参数化HTE估计方法。