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. 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. 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 that 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.
翻译:众所周知,因果模型很难被验证,因为它们对混乱的假设是无法检验的。新的科学实验提供了利用预测性能评估因果模型的可能性。预测性绩效措施通常对因果假设的违反情况具有很强的力度。然而,预测性绩效则取决于培训和测试组的选择情况。偏见培训组可以对模型性能进行乐观的评估。在这项工作中,我们重新审视最近根据Kemmeren基因扰动数据集测试的若干拟议因果模型的预测性能。我们发现,抽样选择偏差可能是模型性能的一个关键驱动因素。我们提议使用一个不太偏差的评价组来评估预测性能并比较这一新组的模型的模型。在这种背景下,因果模型的性能与Lasso等基于协会的标准估计器类似或更差。最后,我们在模拟研究中比较了因果估计器的性能,这些模拟研究复制了基因淘汰实验的Kemmeren结构,但没有任何抽样选择偏差。这些结果使人们更好地了解了若干因果模型的性能,并指导未来研究如何使用Kemmeren。