Liu Tony, Ungar Lyle, Kording Konrad
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Nat Comput Sci. 2021 Jan;1(1):24-32. doi: 10.1038/s43588-020-00005-8. Epub 2021 Jan 14.
Estimating causality from observational data is essential in many data science questions but can be a challenging task. Here we review approaches to causality that are popular in econometrics and that exploit (quasi) random variation in existing data, called quasi-experiments, and show how they can be combined with machine learning to answer causal questions within typical data science settings. We also highlight how data scientists can help advance these methods to bring causal estimation to high-dimensional data from medicine, industry and society.
从观测数据中估计因果关系在许多数据科学问题中至关重要，但可能是一项具有挑战性的任务。在这里，我们回顾了计量经济学中流行的因果关系方法，这些方法利用现有数据中的（准）随机变化，即所谓的准实验，并展示了如何将它们与机器学习相结合，以在典型的数据科学环境中回答因果问题。我们还强调了数据科学家如何能够帮助推进这些方法，以便将因果估计应用于来自医学、工业和社会的高维数据。
