Levis Alexander W, Mukherjee Rajarshi, Wang Rui, Haneuse Sebastien
Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, USA.
Department of Biostatistics, Harvard University, Boston, USA.
Can J Stat. 2025 Jun;53(2). doi: 10.1002/cjs.11832. Epub 2024 Sep 19.
Large observational databases are often subject to missing data. As such, methods for causal inference must simultaneously handle confounding and missingness; surprisingly little work has been done at this intersection. Motivated by this, we propose an efficient and robust estimator of the causal average treatment effect from cohort studies when confounders are missing at random. The approach is based on a novel factorization of the likelihood that, unlike alternative methods, facilitates flexible modelling of nuisance functions (e.g., with stateof-the-art machine learning methods) while maintaining nominal convergence rates of the final estimators. Simulated data, derived from an electronic health record-based study of the long-term effects of bariatric surgery on weight outcomes, verify the robustness properties of the proposed estimators in finite samples. Our approach may serve as a theoretical benchmark against which ad-hoc methods may be assessed.
大型观察性数据库常常存在数据缺失的情况。因此，因果推断方法必须同时处理混杂因素和数据缺失问题；令人惊讶的是，在这个交叉领域所做的工作极少。受此启发，我们提出了一种高效且稳健的估计方法，用于在混杂因素随机缺失时从队列研究中估计因果平均治疗效果。该方法基于一种新颖的似然因子分解，与其他方法不同的是，它便于使用灵活的方法（例如，采用最先进的机器学习方法）对干扰函数进行建模，同时保持最终估计量的名义收敛速度。从一项基于电子健康记录的减肥手术对体重结果的长期影响研究中得出的模拟数据，验证了所提出估计量在有限样本中的稳健特性。我们的方法可作为一个理论基准，用于评估临时方法。
