Abstract There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This growth in interest has led to a number of potential estimators one could use in this setting. Each of these estimators has different operating characteristics, and it is unlikely that one estimator will outperform all others across all possible scenarios. Coupling this with the fact that an analyst can never know which approach is best for their particular data, we propose a synthetic estimator that averages over a set of candidate estimators. Averaging is widely used in statistics for problems such as prediction, where there are many possible models, and averaging can improve performance and increase robustness to using incorrect models. We show that these ideas carry over into the estimation of causal effects in high-dimensional scenarios. We show theoretically that averaging provides robustness against choosing a bad model, and show empirically via simulation that the averaging estimator performs quite well, and in most cases nearly as well as the best among all possible candidate estimators. Finally, we illustrate these ideas in an environmental wide association study and see that averaging provides the largest benefit in the more difficult scenarios that have large numbers of confounders.

中文翻译：

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在高维中平均因果估计量

摘要 近年来，当潜在混杂因素的数量过多时，人们对开发估计因果效应的方法越来越感兴趣。这种兴趣的增长导致了一些可以在这种情况下使用的潜在估计量。这些估计器中的每一个都具有不同的操作特性，并且在所有可能的情况下，一个估计器不太可能胜过所有其他估计器。将此与分析师永远无法知道哪种方法最适合他们的特定数据这一事实相结合，我们提出了一个综合估计器，该估计器对一组候选估计器求平均值。平均在统计中被广泛用于预测等问题，其中有许多可能的模型，平均可以提高性能并增加对使用不正确模型的鲁棒性。我们表明，这些想法可以延续到高维场景中因果效应的估计中。我们在理论上表明平均提供了对抗选择坏模型的鲁棒性，并通过模拟经验表明平均估计器表现得相当好，并且在大多数情况下几乎与所有可能的候选估计器中的最佳估计器一样好。最后，我们在环境范围内的关联研究中说明了这些想法，并看到平均在具有大量混杂因素的更困难的场景中提供了最大的好处。在大多数情况下，几乎与所有可能的候选估计量中最好的一样。最后，我们在环境范围内的关联研究中说明了这些想法，并看到平均在具有大量混杂因素的更困难的场景中提供了最大的好处。在大多数情况下，几乎与所有可能的候选估计量中最好的一样。最后，我们在环境范围内的关联研究中说明了这些想法，并看到平均在具有大量混杂因素的更困难的场景中提供了最大的好处。