论文标题
帕克卡：因果推断，有部分已知的原因
ParKCa: Causal Inference with Partially Known Causes
论文作者
论文摘要
观察数据的因果推断的方法是无法进行反事实数据或实现随机实验的情况的替代方法。采用堆叠方法，我们提出的方法帕克卡（Parkca）结合了几种因果推理方法的结果，以在具有一些已知原因和许多潜在原因的应用中学习新原因。我们在两项全基因组关联研究中验证了Parkca，一个现实世界和一个模拟数据集。我们的结果表明，帕克卡比现有方法可以推断出更多的原因。
Methods for causal inference from observational data are an alternative for scenarios where collecting counterfactual data or realizing a randomized experiment is not possible. Adopting a stacking approach, our proposed method ParKCA combines the results of several causal inference methods to learn new causes in applications with some known causes and many potential causes. We validate ParKCA in two Genome-wide association studies, one real-world and one simulated dataset. Our results show that ParKCA can infer more causes than existing methods.
