Long James P, Ha Min Jin
Department of Biostatistics, University of Texas MD Anderson Cancer Center, Texas, USA.
Stat Anal Data Min. 2022 Feb;15(1):5-14. doi: 10.1002/sam.11559. Epub 2021 Oct 20.
Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction performance measures are typically robust to violations in causal assumptions. However prediction performance does depend on the selection of training and test sets. In particular biased training sets can lead to optimistic assessments of model performance. In this work, we revisit the prediction performance of several recently proposed causal models tested on a genetic perturbation data set of Kemmeren [5]. We find that sample selection bias is likely a key driver of model performance. We propose using a less-biased evaluation set for assessing prediction performance and compare models on this new set. In this setting, the causal models have similar or worse performance compared to standard association based estimators such as Lasso. Finally we compare the performance of causal estimators in simulation studies which reproduce the Kemmeren structure of genetic knockout experiments but without any sample selection bias. These results provide an improved understanding of the performance of several causal models and offer guidance on how future studies should use Kemmeren.
因果模型极难验证，因为它们对混杂因素做出了无法检验的假设。新的科学实验为使用预测性能评估因果模型提供了可能性。预测性能度量通常对因果假设的违背具有鲁棒性。然而，预测性能确实取决于训练集和测试集的选择。特别是有偏差的训练集可能导致对模型性能的乐观评估。在这项工作中，我们重新审视了几个最近提出的因果模型在Kemmeren的基因扰动数据集[5]上测试的预测性能。我们发现样本选择偏差可能是模型性能的关键驱动因素。我们建议使用偏差较小的评估集来评估预测性能，并在这个新集合上比较模型。在这种情况下，与基于标准关联的估计器（如套索回归）相比，因果模型的性能相似或更差。最后，我们在模拟研究中比较了因果估计器的性能，该模拟研究重现了基因敲除实验的Kemmeren结构，但没有任何样本选择偏差。这些结果有助于更好地理解几种因果模型的性能，并为未来研究如何使用Kemmeren数据提供指导。
