Robust causal inference for point exposures with missing confounders

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
Alexander W. Levis, Rajarshi Mukherjee, Rui Wang, Sebastien Haneuse
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引用次数: 0

Abstract

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 state-of-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.

缺失混杂因素的点暴露的稳健因果推理
大型观测数据库常常存在数据缺失的问题。因此,因果推理的方法必须同时处理混淆和缺失;令人惊讶的是,在这个十字路口几乎没有做什么工作。受此启发,我们提出了一种有效且稳健的估计方法,用于随机缺失混杂因素时队列研究的因果平均治疗效果。该方法基于一种新的似然分解,与其他方法不同,它有助于灵活地建模干扰函数(例如,使用最先进的机器学习方法),同时保持最终估计器的名义收敛率。模拟数据来源于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的研究,在有限样本中验证了所提出的估计器的鲁棒性。我们的方法可以作为评估特别方法的理论基准。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
62
审稿时长
>12 weeks
期刊介绍: The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.
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