Multiple imputation and test-wise deletion for causal discovery with incomplete cohort data
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不完整队列数据因果发现中的多重插补与逐检验删除
- 研究问题
- 基于条件独立检验的因果发现面对缺失数据时，逐检验删除与多重插补分别何时能恢复结构，其表现如何随变量类型变化？
- 方法与思路
- 作者建立逐检验删除恢复因果结构的充要条件，讨论多重插补的困难，并在基准因果图模拟及儿童队列数据中比较多种策略。
- 材料陈述的贡献
- 摘要称两类方法均明显优于整行删除和单次插补；少量高斯或离散变量下多重插补较有用，混合变量场景没有统一最优方法。
- 阅读关注点
- 核对结构可恢复条件与缺失机制的关系、条件独立检验和插补模型的兼容性，以及因果图比较指标与混合变量模拟设定。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出充要条件，观察数据恢复因果图仍依赖未在摘要展开的假设。
原文摘要
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focussing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery algorithms rely on conditional independence testing when building the graph. Until recently, these algorithms have been unable to handle missing values. In this paper, we investigate two alternative solutions: Test-wise deletion and multiple imputation. We establish necessary and sufficient conditions for the recoverability of causal structures under test-wise deletion, and argue that multiple imputation is more challenging in the context of causal discovery than for estimation. We conduct an extensive comparison by simulating from benchmark causal graphs: As one might expect, we find that test-wise deletion and multiple imputation both clearly outperform list-wise deletion and single imputation. Crucially, our results further suggest that multiple imputation is especially useful in settings with a small number of either Gaussian or discrete variables, but when the dataset contains a mix of both neither method is uniformly best. The methods we compare include random forest imputation and a hybrid procedure combining test-wise deletion and multiple imputation. An application to data from the IDEFICS cohort study on diet- and lifestyle-related diseases in European children serves as an illustrating example.