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Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration

机译：模拟评估因果发现的重采样方法：整体性能和校准

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摘要

Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a real-world setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that one's choice of which to use should depend on the sample size.
机译：当可以观察到系统但实际上无法进行实验时，因果发现可以成为研究因果关系的强大工具。尽管如此，它很少在任何科学或医学领域中使用。阻止因果发现领域产生较大影响的主要障碍之一是，很难确定在现实环境中何时可以信任因果发现方法的输出。当人们的健康迫在眉睫时，信任尤为重要。在本文中，我们报告了一系列模拟研究的结果，这些研究调查了不同重采样方法的性能，这些方法可作为对发现的图形特征的置信度指标。我们发现子采样和替换采样均表现出令人惊讶的出色表现，这表明它们可以作为对图形特征信心的基础。我们还发现，二次采样和替换采样的校准具有不同的收敛特性，这表明使用哪种采样应该取决于样本量。

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