Bagged random causal networks for interventional queries on observational biomedical datasets.
Bagged random causal networks for interventional queries on observational biomedical datasets.
复制标题
用于观察性生物医学数据集的介入查询的袋装随机因果网络。
DOI：
10.1016/j.jbi.2021.103689
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发表时间：
2021-03
影响因子：
4.5
通讯作者：
Bian J
中科院分区：
文献类型：
--
作者：
Prosperi M;Guo Y;Bian J
从观察数据中学习因果效应，例如通过数据挖掘电子健康记录（EHR）估计治疗对生存的影响，可能会因未测量的混杂因素、中介因素和碰撞因素而产生偏倚。当特征/协变量之间的因果依赖关系以有向非循环图的形式表示时，使用do-calculus可以识别一个或多个调整集，用于在某些假设下消除给定因果查询上的偏差。然而，因果结构的先验知识可能只是部分的;因果结构发现的算法通常提供模糊的解决方案，并且当特征集变大时，它们的计算复杂性变得实际上难以处理。我们假设因果查询对结果的真实因果影响的估计可以近似为复杂度较低的估计量的集合，即袋装随机因果网络。袋装随机因果网络是通过对特征子空间（包括查询、结果和随机数量的其他特征）进行采样，在特征之间绘制条件依赖关系，并推断相应的调整集而构建的子网络的集合。然后可以通过查询与调整集配对的结果的任何回归函数来估计因果效应。通过模拟和真实世界的临床数据集（III类错牙合畸形数据），我们表明，在大多数情况下，如果结构已知，则袋装估计量与真实的因果效应一致，当结构未知时（使用统计学估计），具有良好的方差/偏差权衡，计算复杂性低于学习完整网络，并且优于提升回归。总之，袋装随机因果网络非常适合从EHR和其他高维生物医学数据库的观察性研究中估计查询目标因果效应。
Learning causal effects from observational data, e.g. estimating the effect of a treatment on survival by data-mining electronic health records (EHRs), can be biased due to unmeasured confounders, mediators, and colliders. When the causal dependencies among features/covariates are expressed in the form of a directed acyclic graph, using do-calculus it is possible to identify one or more adjustment sets for eliminating the bias on a given causal query under certain assumptions. However, prior knowledge of the causal structure might be only partial; algorithms for causal structure discovery often provide ambiguous solutions, and their computational complexity becomes practically intractable when the feature sets grow large. We hypothesize that the estimation of the true causal effect of a causal query on to an outcome can be approximated as an ensemble of lower complexity estimators, namely bagged random causal networks. A bagged random causal network is an ensemble of subnetworks constructed by sampling the feature subspaces (with the query, the outcome, and a random number of other features), drawing conditional dependencies among the features, and inferring the corresponding adjustment sets. The causal effect can be then estimated by any regression function of the outcome by the query paired with the adjustment sets. Through simulations and a real-world clinical dataset (class III malocclusion data), we show that the bagged estimator is –in most cases– consistent with the true causal effect if the structure is known, has a good variance/bias trade-off when the structure is unknown (estimated using heuristics), has lower computational complexity than learning a full network, and outperforms boosted regression. In conclusion, the bagged random causal network is well-suited to estimate query-target causal effects from observational studies on EHR and other high-dimensional biomedical databases.
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